- Email: [email protected]
Help wanted!: A researcher’s guide to utility-university collaborations
The Electricity Journal 32 (2019) 106680
Contents lists available at ScienceDirect
The Electricity Journal journal homepage: www.elsevier.com/locate/tej
Help wanted!: A researcher’s guide to utility-university collaborations Andrew Butts*, Julia Wilber, Stephen Rose
T
Energy Transition Lab, University of Minnesota, United States
ARTICLE INFO
ABSTRACT
Keywords: R&D University-industry Collaborative research Innovation Case studies
Electric utilities have long collaborated with academic researchers, but the lifecycle of these relationships has not been studied systematically. We fill that gap by interviewing utility personnel and quantitatively analyzing papers published by utility-university collaborations. This work suggests best practices for initiating and ensuring the success of utility-university collaborations, while also commenting on the range of collaboration opportunities.
1. Introduction and background Current analysis indicates industrialized countries must rapidly and deeply reduce their emissions of greenhouse gases (GHG) to avoid catastrophic global climate change (IPCC, 2018). The electric power industry has reduced GHG emissions farther and quicker than any other sector, but it faces increasing challenges as it continues decarbonizing. The low marginal cost of renewable energy, along with other factors, is driving the retirement of many fossil-fuel and other thermal power plants that help compensate for renewables’ variability. An increasing portion of new renewable generation comes from small, distributed generators beyond the control of utilities. The electrical grid infrastructure is aging and requires large investments. The electricity industry workforce is also aging: a 2015 survey found roughly 25 % of the electric utility workforce will be ready to retire in the next 5 years (DOE, 2017). Despite these technical and labor challenges, investorowned utilities must find ways to continue to deliver profits to their shareholders and cooperative and municipal utilities must continue to meet the needs of their member-owners amid the changing business environment. This will require innovation and cooperation. Research and development (R&D) is the usual source of innovation, but electric utilities spend significantly less on R&D than firms in other industries (Costello, 2016a; Grubler et al., 2012). Regulation discourages investment in R&D by investor-owned utilities (IOUs) that serve the majority of the electric load in the U.S. As regulated monopolies, they do not face competition and innovations risk devaluing their own assets (Costello 2016a, 2016b). More importantly, these regulated utilities may only recover the cost of investments deemed “prudent” by regulators, which discourages risk-taking (Lyon, 1995; National Academies of Sciences, Engineering, and Medicine, 2016).
⁎
Corresponding author. E-mail address: [email protected] (A. Butts).
https://doi.org/10.1016/j.tej.2019.106680
Available online 29 November 2019 1040-6190/ © 2019 Elsevier Inc. All rights reserved.
Additionally, since allowable profits are tied to the costs of the investment, rather than the benefits, utilities are unable to capture the “upside” of successful investments (Costello, 2016b). However, utilities in competitive, deregulated markets innovate even less than regulated monopolies, preferring to cut costs rather than take risks with long-term investments (Sanyal and Cohen, 2008). Despite those limitations, many utilities previously maintained close, long-term relationships with their local universities to support research and cultivate future employees; however, direct research funding from utilities to universities and indirect funding though the Electric Power Research Institute (EPRI) declined sharply in the 1980s and ‘90s (Russell, 2010). Little research exists on research collaborations between universities and electric utilities. Previous work on these collaborations specifically focuses on technology adoption by utilities, rather than collaborative new technology development (Block et al., 1990; Dedrick et al., 2014; Lester and Hart, 2011; Strong, 2017). Research on utilities’ R&D activities focuses primarily on two aspects: (1) structural and organizational determinants, and (2) factors that affect the success of research collaborations with universities. Larger IOU’s tend to adopt technologies sooner than smaller and publicly-owned utilities (Rose and Joskow, 1990) and are more likely to conduct internal R&D (Sanyal and Cohen, 2008). Utilities describe individual researchers’ specialized expertise and ability to integrate expertise from other fields as universities’ unique strengths (Ware, 1986). However, university researchers familiarize themselves with the details of specific research more slowly than consultants or laboratories, partly because of the cycles of the academic year (Ware, 1986). By contrast, there has been extensive study of university collaborations with non-utility industrial partners. Firms’ primary motivations for collaborating with academics are gaining access to new knowledge and
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
ideas, developing new products, and solving specific problems (Feller et al., 2002; Lee, 2000; Perkmann and Walsh, 2007). Many firms also report a desire to develop and recruit new employees (Feller et al., 2002; Perkmann and Walsh, 2007; Siegel et al., 2003). When firms collaborate with universities, interpersonal relationships are the main conduit for exchanging knowledge (Feller et al., 2002; Oliver and Liebeskind, 1997) and commercializing university-developed technologies ((Siegel et al., 2003). Barnes et al. (2002) find effective projects require matched collaborator interests, appropriate resources, mutual benefits, high-quality project management, and flexibility to accommodate changes. Correspondingly, Siegel et al. (2003) find the most significant barriers are culture clashes, bureaucratic inflexibility, poorly designed reward systems, and ineffective management of university technology transfer offices. University researchers are motivated to collaborate with industrial partners to secure research funding, do research that will gain them profesional recognition, get insights into their own research, and test applications of theory (Lee, 2000; Siegel et al., 2003). Our work refines the research by specifically examining electric utilities to understand why and how they conduct R&D with universities. We begin with a quantitative analysis of publications by joint utilityuniversity teams to reveal patterns in proximity and longevity of their partnerships. We complement the quantitative analysis with qualitative interviews of utility participants in those research projects. These interviews reveal how they find their collaborators, why they collaborate with universities, what factors contribute to success or failure, and where opportunities exist to expand research. This research investigates whether previous findings in other industries apply to the unique context of electric utilities. The answer, largely, is yes. Our findings begin to fill this gap in the literature, and also offer advice for researchers seeking utility collaborations.
2014). The thickness of lines connecting institutions is proportional to the number of shared collaborations. This diagram shows the 17 utilities with 10 or more eligible publications and the top 30 universities, ordered by publication quantity. The “Other” sector lumps together 93 additional universities that have fewer than 10 publications with utilities. Utilities and Universities are ordered (and drawn) from largest to smallest in a clockwise fashion. The transparency of the outer ring differentiates “local” [ < 160 miles] links (opaque), from national links (translucent), and international links (transparent). 2.2. Interviews The qualitative portion of this work is based on semi-structured interviews with seven utility-employed participants (typically engineers or low-level managers) in U-U collaborations. These interviews collect detailed examples and tacit insight into the nature of these collaborations that compliment the quantitative analysis (which excludes collaborations that did not result in publication), but they are not intended to provide a comprehensive overview. Further research should seek to explore other modes of collaboration and a broader range of utility sizes and types. We selected the initial pool of interview candidates as utility personnel who collaborated on several publications in the last 10 years, drawn from the U-U publication dataset. Additional interview candidates were identified by "snowball sampling," i.e. referral by colleagues (Morgan, 2008) and from our own personal networks. We contacted candidates by email and a total of seven people from three investorowned utilities (“IOUs”) agreed to be interviewed, a 20 % success rate. The interviewees we selected worked on various topics: data science, economic modeling, engineering, and business innovation. Interviews were conducted by telephone or in-person, lasted approximately one hour, and were recorded and transcribed. We used a series of open-ended questions designed to identify: (1) types of research well-suited to U-U collaborations, (2) gaps between utility needs and university research (3) unique value of university research to utilities, and (4) reasons why research collaborations either succeed or fail. A complete list of questions is given in Appendix A. We asked interview subjects to first answer the questions about a specific research project they were directly involved in, then asked them to generalize about the representativeness of that experience compared to other projects they participated in or know of (Lee, 2000). We analyze the interviews using a combination of inductive and deductive approaches to qualitative coding. Three broad codes were initially selected to group the different types of data obtained by our list of questions: Project Chronology, Subject of Discussion, and Characteristics of Challenges. Codes were then subdivided into natural categories, i.e. Project Chronology was divided into Initiation, PreProject, During Project, and Post-Project. Open coding was then used within categories to begin conceptualizing the data and identifying thematic connections. Many of the resulting codes were either sub-divided further or reconfigured around a new concept. After reviewing the hierarchy of coding we developed, we then iteratively developed new categories around useful concepts that addressed the key goals of the research in a new hierarchy. All existing codes were able to be resorted into these new categories, which form the framework of our qualitative results sections. A complete codebook of our two coding hierarchies is included in Appendix B.
2. Methods 2.1. Quantitative analysis of publications To generate our initial dataset of University-Utility (U-U) publications, we query the “author affiliation” field in the Scopus database for articles published between 1930 and 2018 that include at least one author affiliated with a large U.S. utility. We select the 20 largest utilities in terms of 2016 revenue listed in Appendix A (T10 in EIA, 2018). The results for each utility include publications under previous company names, by their current subsidiaries or local operating companies, and by utilities they acquired. We exclude several utilities listed in the EIA data that do not appear in the Scopus “Author Affiliations” field (Puget Sound Energy Inc, Long Island Power Authority, and Reliant Energy Retail Services). We then filter the Scopus query results to include only publications that include authors with “University”, “College”, “School” or “Institute” affiliations, which were then manually verified as post-secondary institutions. The publications are further filtered to remove duplicates, including work published under the same title in a conference proceedings and a peer-reviewed journal. For authors who have both university and utility affiliations, we assume the utility is the primary affiliation except in cases when we can determine from the author’s publically-available biographical information that he or she did the research as a student but subsequently submitted or published it while working for a utility. This yields 1020 publications that include both a utility and university author (Butts, 2019). In order to analyze co-authors’ proximity to each other, we assume each author is geographically located at the headquarters of his or her listed institutional affiliation unless some other location is specified. This may mis-identify the locations of employees of local subsidiaries of multi-state utility holding companies. Our analysis of author proximity excludes 17 publications that include authors from multiple universities and multiple utilities. Institutional connections are illustrated by a chord diagram in Fig. 1, created using the “circlize” software package for R (Z. Gu et al.,
3. Results 3.1. How do utilities find academic collaborators? Most collaborations we examine grew from social or professional connections, rather than a deliberate search for a collaborator with specific capabilities. One utility says “it started out [as] just a networking connection between one or two engineers at [the utility] and 2
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
Fig. 1. A chord diagram of publications from collaborations between electric utilities and universities. The thickness of each line represents the number of collaborative publications. Lines of weight 2 or 1 were rendered transparent for overall clarity.
Fig. 2. A (partially stacked) histogram of publication frequency by distance between the closest pair of utility and university collaborators. The first bar is stacked to indicate the portions occurring under 40 miles, and between 40 and 160 miles. 3
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
[a professor], where they had a professional relationship, probably through an IEEE standards group, and talked about ways that they could potentially work together.” (3A) This type of informal brainstorming leading to collaboration was common among our interviewees. We find only one instance of a utility employee seeking unknown researchers with specific expertise, which was achieved through referral via their personal network. The majority of our interviewees describe collaborations with nearby universities. However, our analysis of U-U publications shows a weaker preference for local collaborations. Just half of the 1020 publications include individuals less than 160 miles apart, and only 34 % include individuals less than 40 miles apart. The geographic connection is stronger between institutions: twelve of the seventeen largest U.S. utilities we examine collaborate most frequently with a university within 160 miles, and more than half (9 of 17) collaborate most frequently within 40 miles. The outer ring of Fig. 1 differentiates “local” [ < 160 miles] collaborations (opaque), from national collaborations (translucent), and international collaborations (transparent). Fig. 1 also shows significant differences in diversity of utilities’ institutional collaborators. Interestingly, our quantitative analysis shows frequent collaborators (≥5 publications together) are less likely to be near each other than UU collaborators in general. Only 29 % of these prolific pairs are within 160 miles of one another. One interviewee with repeat collaborations explains: “[Nearby U.] might as well be [Distant U.]. I'm not going up to [Nearby U.] 25 miles away. With computers and email, it's as easy to work with someone in [Distant U.] as it is [Nearby U.] for me.” (3B) The fact that single-publication collaborations are more common for nearby collaborators (Fig. 2) suggests that although shorter distances may make starting collaborations easier, distance does not seem integral to the endurance of repeat-collaborations between individuals. Alternatively, the stronger connection between local institutions than individuals may suggest that university researchers instead collaborate with several different people at a nearby utility. This is consistent with several interviewees, who describe introducing academic collaborators to other departments within their utility after their collaboration ends. The publication-author data we analyze suggests the number of publications and number of authors and organizations per publication are increasing over time. Future research could augment our data (Butts, 2019) with data on R&D funding and changes in grant evaluation criteria that may help explain or control for these observed trends. Our publication data also shows the distances between collaborators is increasing over time. Future research could test hypotheses to explain these trends, including examining whether this reflects the persistence of collaborations as researchers transfer to different universities.
cases we examine, the university approached the utility first. Only one interviewee describes the opposite: deliberately seeking academic researchers with specific capabilities. In that case, the interviewee wanted the study to be done by an academic institution, which regulators would find more credible than a consultant. Most interviewees also describe wanting to contribute to the “pipeline” of new skilled workers. Interestingly, they describe wanting to cultivate talent not just for their own utility, but for the broader utility industry. One says “[We’re] hoping and expecting that [these students] come into industry, maybe not directly to work for us but they're strengthening the greater industry” (3A) and another explains “those are the people that are going to bubble up in the field and become the next generation of researchers and consultants” (2A). 3.3. What makes collaborations succeed or fail? Interviewees consider collaborations most successful when they meet a business need for their utility. As described above, interviewees typically match the capabilities of researchers who approached them to utility business needs, rather than deliberately seeking researchers with specific capabilities. Interviewees prefer researchers who approach them offering capabilities, which are easier to match than projects. One interviewee likes a professor’s initial pitch, which he/she remembers as “These are our skills. Share with us your areas of interest needs and pain points” (1A). Another says he/she found a successful match “not [by] taking the specific [project] recommendations of the university group” but by finding a group within the utility that needed their “skills in managing big data sets and building models to extract information from them” (1C). In contrast, utilities are less enthusiastic about being approached with a researcher’s favored project or approach: “Utilities are not starved for ideas. People [are always] knocking at the door, saying ‘Hey, I’ve got this great widget’,” (1A) while another opines, “They have this vision they want to sell us, but very often it doesn't align with what we actually need.” (2A) Most interviewees also describe being approached by researchers requesting data, and suggest they would be more receptive to those requests if they meet a business need. One says, “If [academics] want data from the utility, they should come and ask 'What can we do for you?', in addition to what they want to do in their own research track.” (2A) While meeting a business need is regarded as critical to success, there was no consensus whether university collaborations are better suited to narrowly-defined projects or more open-ended research. Many interviewees describe successes and failures with both kinds of research, but agree that consultants are ill-suited to open-ended work. Interviewees with an academic affinity are more optimistic about openended research, and praise the unexpected insights that can arise. One explains: “…the most impactful research findings are not those that you would anticipate at the outset. You need to be able to follow your nose a little bit.” (2A) Reading into the context of open-ended research disappointments, most seem to involve inexperienced student workers or poor project management. This is unsurprising, as project management skills were identified as critical to any collaboration. Legal negotiations pose a substantial barrier to U-U collaborations, though no interviewees described them as insurmountable. The negotiations are often time-consuming because they must reconcile utilities’ and universities’ standard “boilerplate” contracts. In some cases, timeconsuming negotiations have derailed timing-sensitive projects. If the start of a project is delayed, the continuity of student researchers may be disrupted by graduation or professors may become distracted by teaching responsibilities. Additionally, the significant negotiating time and effort may deter a potential utility collaborator, especially if they don’t see a compelling business case. Even legal issues that don’t require negotiation can be a barrier to collaboration. Nearly every interviewee specifically mentions non-disclosure agreements (NDA), data security protocols, and a concern that university researchers are unfamiliar with or disinterested in them.
3.2. What motivates utilities to collaborate with university researchers? We find most of our interviewees are motivated, in part, to collaborate with universities by what we term affinity for academic research. This usually stems from their prior experience doing research at a university (often as a graduate student), or from positive experiences collaborating with universities. An interviewee and former postdoctoral researcher says “I wanted to help… I would love to see a day where we have a research objective and instead of going to a consultant, we go to a university.” (2A). Personal affinity for academic research is significant because a utility “is made up of individuals and those individuals will make decisions about whether or not they strike up a collaboration with a consultant or a university or a national lab." (2A) This affinity reveals itself through interviewees who found convenient matches between their business needs and the capabilities of researchers they or their coworkers already knew rather than deliberately searching for researchers with specific capabilities. An interviewee says “I can almost assure you [utility] did not reach out and scan a bunch of academic institutions to try to figure out who to go to with all of these data … That's not our model. Our model, if we need to do something like that, is to hire a consultant, not an academic institution.” (2A). In half the 4
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
3.4. Project management
ended types (data science, business innovation, policy modeling), are well-suited to universities and expected to thrive in the current industry climate. This work is also the first quantitative analysis of universityutility collaborations, and it shows that local collaborations more often produce one-off collaborations but perennial collaborations tend to flourish from favorable conditions beyond mere proximity. The quantitative and qualitative analyses in this work complement each other, painting a more complete picture of utility-university collaborations than either method alone. Our quantitative results reveal that repeat collaborators tend to be farther apart than average, while our interviews reveal that geographic proximity is less important than good project management and meeting a utility business need. Future research could use the quantitative data we compiled (Butts, 2019) to analyze the effect of those factors on the number of publications using network analysis of connections between co-authors. However, interviewees describe types of research collaborations that do not appear in our quantitative dataset (failed projects, projects without publications, practicum projects, and consulting), which indicates analyzing publications under-estimates collaborations between universities and utilities. They also talk about non-research interactions between utilities and universities, such as giving guest lectures or teaching courses, giving strategic advice, and participating in regulatory proceedings. Finally, interviewees mention topics that rarely appear in the publication record, such as new business models for utilities, which suggests an unmet need for academic research. For university researchers interested in working with electric utilities, our research offers several insights. First, it is often fruitful to just ask. Invite utility personnel for a lab tour or offer to give a presentation. Academic researchers typically meet them through social or professional networks, and find that utility personnel who have done academic research themselves tend to be more receptive to collaborating. “Boundary organizations” that straddle the boundaries between academia and utilities and between academic disciplines can also facilitate those connections (Guston, 2001). Our research was initiated to inform the strategic direction of one such boundary organization, the Institute on the Environment at the University of Minnesota. Second, when discussions begin, emphasize your research capabilities rather than proposing a particular project. The initial connection may not necessarily lead to a collaboration, but that person may be able to connect you to someone else in the utility who needs those capabilities. Finally, utilities highly value researchers who effectively manage projects. Communicate regularly with the utility project sponsor to address problems early and ensure the project stays on-schedule. Utilities face great challenges that academic research can help address and universities seek research topics, funding, and data that utilities may be able to provide. More broadly, universities have a mission to apply their knowledge and expertise to real-world problems. The electric power system and the electricity industry are changing rapidly and in unprecedented ways. The IPCC predicts global GHG emissions must fall 45 % by 2030 and reach “net zero” by 2050 to limit global warming to 1.5 °C and mitigate climate catastrophe (IPCC, 2018). Countries such as Germany, U.S. states such as California, and U.S. electric utilities such as Xcel Energy have adopted policies to achieve those goals. That will require radical changes to electrical generation and widespread electrification of transportation and heating, which puts electric utilities at the forefront of this change. Academic researchers can assist electric utilities in this transformation. Our research finds that utilities see many topics on which they can collaborate with universities collaborations, and many utility employees welcome researchers who introduce themselves, describe what they can do, and ask, “What can we do for you?”
Many interviewees describe academics as being inexperienced with project management, although they admit utilities could also improve. One says “those people probably are fairly guaranteed to be super smart and capable, but are they going to get your project done or not?” (1C). Several speculate about their own PM missteps, wondering if prior projects would have gone better: “Maybe if we… were a bit more proactive in managing … and keeping the project schedule on track…” (1C). But successful repeat collaborators often share project management responsibilities across their respective organizations. The utility personnel we interview identify three project management functions that are particularly important to the success of research collaboration: Consistent Communication, Connecting Resources, and Timeline Management. Consistent communication is important to identify problems early and ensure the work doesn’t lose sight of business needs. Connecting collaborators to resources, such as utility data and subject matter experts in other fields or departments, helps overcome the “siloed” nature of universities and utilities. Managing schedules and the project timeline is crucial because business processes and academic calendars operate on incongruous cycles. Whether vacations, graduations, or competitive bidding requirements, interviewees observed setbacks often snowballed if slippage wasn’t properly communicated and managed. 3.5. Opportunity areas for university-utility collaborations Our interviewees describe several challenges well-suited to universities. Most say they need help analyzing the “tidal wave” (2A) of data they collect because “utilities aren't typically very well staffed with [data scientists] and they're expensive … ‘big data’ capabilities really fills a gap for us.” (1C) Several also look for help adapting to “evolving regulatory changes” and finding “future business models and revenue making opportunities” (1A). Utilities’ regulatory environments make these innovations difficult to test, but one interviewee describes how regulators created a “regulatory sandbox… to demonstrate and test the new business models.” (1A) The majority of U-U research is published in engineering journals (> 57 %). Accordingly, several interviewees mention U-U engineering projects involving simulation, modeling, or specialized hardware testing; which helps “de-risk” designs before piloting hardware on the actual grid. (1A) Hardware research can be easier to manage compared to less tangible research because the deliverables are easier to define and measure, but university-designed solutions often prove difficult to commercialize at scale. One interviewee states “universities are not in the business of manufacturing and selling you anything. … I want to buy it from somebody that could warranty it and service it… and it’s not just one or two… I need like a hundred” (1A). 4. Conclusions and discussion Faced with a rapidly evolving business and regulatory environment, universities can help utilities adapt to the uncertain future by addressing needs in the present. This is the first investigation into the lifecycle of research collaborations between electric utilities and universities: how and why they begin and what fosters success or failure. We find collaborations typically sprout from social connections. It is often academics who plant the seed of a collaboration; they introduce their capabilities by, for example, giving a presentation at a utility or inviting them for a lab tour. Potential collaborations find fertile ground in utility personnel who have an affinity for academic research and a desire to cultivate budding engineers and analysts for the industry. Meeting a utility’s business need, more than intellectual cleverness, will nurture a collaboration’s success. Effective project management, particularly consistent communication, ensures collaborations bear the desired fruit. Many varieties of research, including intangible (simulation, testing) and open-
Acknowledgements This research was supported by the Institute on the Environment, University of Minnesota, United States, DG-0002-17. 5
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
Appendix A Queries of Scopus database to find utility-university publications We query the “author affiliation” field in the Scopus database for articles published between 1930 and 2018 that include at least one author affiliated with a large U.S. utility. This is 20 separate queries– one for each utility. We select the 20 largest utilities in terms of 2016 revenue, listed in the table below. We exclude several utilities listed in the EIA data that do not appear in the Scopus “Author Affiliations” field (Puget Sound Energy Inc, Long Island Power Authority, and Reliant Energy Retail Services) and we combined utilities that are listed separately in the EIA data but are owned by larger holding companies (e.g. Duke Energy) or have merged (e.g. Public Service Company Colorado and Northern States Power Company). We then filter the combined Scopus query results to include only publications that include at least one author with “University”, “College”, “School” or “Institute” in the name of his or her affiliation. University-affiliated research institutes were counted as universities, independent research institutes such as EPRI were not. This yielded 1296 publications, available at (Butts, 2019). Alabama Power Co Union Electric Co - (MO) (Amren) Arizona Public Service Co Commonwealth Edison Co (Commonwealth Edison Co; ComEd) Consolidated Edison Co-NY Inc Consumers Energy Co DTE Electric Company Duke Energy (Duke Energy Carolinas; Duke Energy Progress; Duke Energy Florida; Duke Energy Indiana) Entergy Louisiana LLC (Entergy Corporation) Georgia Power Co Pacific Gas & Electric Co
PacifiCorp Xcel Energy (Public Service Co of Colorado) Public Service Elec & Gas Co (Public Service Enterprise Group Inc) Salt River Project South Carolina Electric & Gas Company (SCANA) Southern California Edison Co TXU Energy Retail Co LP Virginia Electric & Power Co (Dominion Virginia Power Co) Wisconsin Electric Power Co Northern States Power Co - Minnesota (Xcel Energy)
We use a computer script to parse the text of the author affiliations into fields for each individual author, the institutions with which they are affiliated, and the locations of those institutions. We then manually cleaned the data to fix incorrectly-parsed affiliations. When possible, we found affiliations missing from the Scopus database in the text of published version of the paper. In cases where a single author listed an affiliation with both a utility and university, we used the author’s publicly-available biographical information (typically a resume/CV) to infer which affiliation was their primary. If a publication was submitted when the author was affiliated with a university (typically as a student), we used the university affiliation. For other multiple-affiliation authors, we used the author’s listed address to determine primary affiliation. When that was not available, we assumed the author’s primary affiliation was the utility and that their university affiliation was something akin to “adjunct”. This excluded 216 publications, yielding 1080, available at (Butts, 2019). Finally, we excluded 60 duplicate publications that reflect both a paper and a conference presentation, even in the cases where the names were not a perfect match. This yields 1020 unique publications that include both utility and university authors, available at (Butts, 2019). We calculated distances between collaborators based on their listed addresses or, when those were not available, the addresses of their primary affiliations. This may mis-locate some authors, especially employees of utility holding companies that span several states. For publications with multiple collaborating institutions, we used the minimum distance between a university and a utility. The 17 publications from multiple utilities and multiple universities were excluded. Appendix B Interview questions We asked interviewees to focus their answers on a specific collaboration in which they had been involved. Later we asked them to generalize their experience and compare it to other collaborations in which they had been involved. We asked the following questions in the semi-structured interviews: 1 2 3 4 5 6 7 8 9 10
How did this relationship begin and how did it evolve into an actual research collaboration? How did the relationship evolve into a research project? What conditions were necessary? What barriers/challenges did you face? What challenges or problems arose in this project? If the project finished, what relationship do you now have this university? How does your experience collaborating with academic researchers compare to collaborations with other research institutes, government agencies, or consultants? What are the advantages and disadvantages of working with a university? Do have any advice or a utility employee considering a collaboration with a university? What kinds of problems utility problems do you think are well suited to collaboration with universities? Are there any questions I should have asked? Is there anything you want other people (especially, but not limited to, academic researchers) to know? Qualitative Codes for analyzing interviews We coded passages of the transcribed interviews with the following codes: Initial codes
1 Miscellaneous a Interesting But Unclassified b Memorable Quotes 2 Project Chronology 6
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
3 4 5 6
7
8
a Deliverables and Post Project i Follow-on Relationship 1 Continued 2 Ended 3 Expanded ii Success Level 1 Failure 2 Partial 3 Total b During Project i Communication ii Continuity 1 Individual Continuity 2 Skill Continuity iii Hurdles 1 Eased or Facilitated 2 Impeded or Slowed iv Project Management v Shift in Expectations vi Timeline c Initiation i Initiator 1 University Initiated 2 Utility Initiated ii Motivation of Initiation 1 Advantages of academic research Labor New knowledge Other advantage Specific capabilities 1 1 1 Attitude toward academic research 2 Workforce development pipeline 2 Type of Introduction 1 Exploratory Introduction 2 Social Introduction 2 Pre-Project i Business Process 1 Schedule or Timeline ii Definition of Contributions of Each Party 1 Connections 2 Equipment 3 Funding 4 Knowledge 5 Motivation 6 Prior Experience 7 Skills iii Definition of Need-Deliverables iv Legal Stuff 1 Data Sharing Agreements 2 NDA 3 Ownership of Results Subject of Discussion a Non-University i Company ii Government Body iii Research Organization b University c Utility Utility Challenges Amenable to University Research a Characteristics i Risk 1 Interfering 2 Non-Interfering ii Technical Maturity 7
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
1 Applied 2 Early Stage 3 Implementation b Topics i Hardware and Testing ii Modeling or Simulation iii Process iv Public Policy Second-round codes 1. Introduction & Background a. University Collaborations with Industry b. What's special about utilities 2. What Motivates Utilities to Collaborate? a. Advantages of Collaborating with Universities b. Personal Receptiveness or Attitude Towards University Research c. Pipeline for New Employees 3. How do utilities find collaborators? a. Evidence for Local Collaborations b. Stories about how they met c. Evidence for Connection to Alma Mater d. Evidence for Repeat Collaborations 4. What impedes a prospective collaboration? a. Legal Negotiations i. Legal Overview ii. Data Sharing iii. Data Security iv. IP or Commercialization b. Business Processes i. Procurement ii. Timeline iii. Other 5. What Makes Collaborations Succeed or Fail? A. Matching i. Matching – Interests,_Capabilities,_Needs ii. Capabilities a. Kid Gloves b. Technical iii. Interests a. Incentives – Continuity iv. Needs v. Financial Arrangements vi. Meeting a Need B. Project Definition i. Deliverable Definition ii. Flexibility or Improvisation iii. Publication C. Project Management i. Accountable Support ii. Project Management iii. Communication iv. Right People - SME's D. Opportunity Areas i. Qualities of collaborations ii. De-risking iii. Commercialization iv. Engineering and Hardware v. Modeling, Data, and Business 6. Conclusions and Discussion a. Advice for Professors b. Advice for Universities c. Real vs. Perceived Problems d. Potential Questions for a follow-on survey
8
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
References
National Academies of Sciences, Engineering, and Medicine, 2016. The Power of Change. National Academies Press, Washington, DC. https://doi.org/10.17226/21712. Oliver, A.L., Liebeskind, J.P., 1997. Three levels of networking for sourcing intellectual capital in biotechnology: implications for studying interorganizational networks. Int. Stud. Manag. Organ. 27 (4), 76–103. https://doi.org/10.2307/40397388. Perkmann, M., Walsh, K., 2007. University–industry relationships and open innovation: towards a research agenda. Int. J. Manag. Rev. 9 (4), 259–280. https://doi.org/10. 1111/j.1468-2370.2007.00225.x. Rose, N.L., Joskow, P.L., 1990. The diffusion of new technologies: evidence from the electric utility industry. Rand J. Econ. 21 (3), 354. https://doi.org/10.2307/ 2555614. Russell, B.D., 2010. Educating the workforce for the modern electric power system. Bridge 40 (1), 35–41. Sanyal, P., Cohen, L.R., 2008. Powering progress: restructuring, competition, and R&D in the U.S. Electric utility industry. Electr. J. 30 (2), 41–79. https://doi.org/10.2307/ 41323233. Siegel, D.S., Waldman, D.A., Atwater, L.E., Link, A.N., 2003. Commercial knowledge transfers from universities to firms: improving the effectiveness of university–industry collaboration. J. High Technol. Manag. Res. 14 (1), 111–133. https://doi.org/10.1016/S1047-8310(03)00007-5. Strong, D.R., 2017. Determinants of early smart meter diffusion in the US electric power industry. Ssrn Electron. J. https://doi.org/10.2139/ssrn.2942960. Ware, B.J., 1986. High voltage engineering - electric Utility/University research interactions in the USA. Power Syst. IEEE Trans. 1 (4), 207–210. https://doi.org/10.1109/ TPWRS.1986.4335045.
Barnes, T., Pashby, I., Gibbons, A., 2002. Effective university – industry interaction: a multi-case evaluation of collaborative R&D projects. Eur. Manag. J. 20 (3), 272–285. Block, R.B., Beardsworth, E., Chan, C., 1990. An evolving technology-transfer system in an R&D consortium: the EPRI approach. J. Technol. Transf. 15 (1-2), 17–24. https:// doi.org/10.1007/BF02377653. Butts, A., 2019. Utility-University Collaboration Publication Data. Mendeley Data. https://doi.org/10.17632/87y67tnxtf.1. Costello, K.W., 2016a. A Primer on R&D in the Energy Utility Sector (No. 16-05). National Regulatory Research Institute, Silver Spring, MD, pp. 1–52. Costello, K.W., 2016b. R&D and public utilities. Electr. J. 29 (5), 19–26. https://doi.org/ 10.1016/j.tej.2016.06.001. Dedrick, J., Venkatesh, M., Stanton, J.M., Zheng, Y., Ramnarine-Rieks, A., 2014. Adoption of smart grid technologies by electric utilities: factors influencing organizational innovation in a regulated environment. Electron. Mark. 25 (1), 17–29. https://doi.org/ 10.1007/s12525-014-0166-6. DOE, 2017. The Electricity Workforce: Changing Needs, New Opportunities. In Transforming the Nations Electricity System The Second Installment of the Quadrennial Energy Review. energy.gov. EIA, 2018. Annual Energy Outlook 2018. US Energy Information Administration., Washington, DC, pp. 1–74. Feller, I., Ailes, C.P., Roessner, J.D., 2002. Impacts of research universities on technological innovation in industry: evidence from engineering research centers. Res. Policy 31 (3), 457–474. https://doi.org/10.1016/S0048-7333(01)00119-6. Grubler, A., Aguayo, F., Gallagher, K., Hekkert, M., Jiang, K., Mytelka, L., et al., 2012. Policies for the energy technology innovation system (ETIS). In: Johansson, T.B., Nakicenovic, N., Patwardhan, A., Gomez-Echeverri, L. (Eds.), Global Energy Assessment (GEA). Cambridge University Press, Cambridge, pp. 1665–1744. https:// doi.org/10.1017/CBO9780511793677.030. Gu, Z., Gu, L., Eils, R., Schlesner, M., Brors, B., 2014. Circlize implements and enhances circular visualization in R. Bioinformatics 30 (19), 2811–2812. https://doi.org/10. 1093/bioinformatics/btu393. Guston, D.H., 2001. Boundary Organizations in Environmental Policy and Science: An Introduction. Science, Technology, & Human Values 26 (4), 399–408. https://doi. org/10.1177/016224390102600401. IPCC, et al., 2018. In: Masson-Delmotte, V., Zhai, P., Pörtner, H.O., Roberts, D., Skea, J., Shukla, P.R. (Eds.), Global Warming of 1.5°C, Geneva. Lee, Y.S., 2000. The sustainability of university-industry research collaboration: an empirical assessment. J. Technol. Transf. 25 (2), 111–133. https://doi.org/10.1023/ A:1007895322042. Lester, R.K., Hart, D.M., 2011. Unlocking Energy Innovation. MIT Press. Lyon, T.P., 1995. Regulatory hindsight review and innovation by electric utilities. J. Regul. Econ. 7 (3), 233–254. https://doi.org/10.1007/BF01067096. Morgan, D.L., 2008. Snowball sampling. In: Given, L.M. (Ed.), The SAGE Encyclopedia of Qualitative Research Methods. SAGE Publications, Inc, Thousand Oaks. https://doi. org/10.4135/9781412963909.n425. p. 816.
Andrew Butts “Andrew Butts is a graduate research assistant at the Institute on the Environment and attends the Humphrey School of Public Affairs at the University of Minnesota while pursuing his Master of Science, Technology, and Environmental Policy. He previously earned his BBA from UW-Madison, studying Operations Management and Communication Arts. He is a core organizer for the Twin Cities’ Science for the People chapter. He hopes to integrate his skills in analysis, communications, and policy to engage the public on society’s greatest challenges." Julia Wilber “Julia Wilber is a graduate research assistant at the Institute on the Environment and attends the Humphrey School of Public Affairs at the University of Minnesota while pursuing a dual Master of Public Policy and Master of Social Work, with a focus on Human Rights and Mental Health. She previously earned her BA from Hamilton College, with an Interdisciplinary Concentration on Social Justice, Peace, and Development." Stephen Rose “Stephen Rose is a postdoctoral researcher at the Institute on the Environment and the Humphrey School of Public Affairs at the University of Minnesota, investigating long-term investment planning by electric utilities. He earned a Ph.D. in Engineering & Public Policy from Carnegie Mellon University and previously designed control systems for wind turbines at G.E."
9
Contents lists available at ScienceDirect
The Electricity Journal journal homepage: www.elsevier.com/locate/tej
Help wanted!: A researcher’s guide to utility-university collaborations Andrew Butts*, Julia Wilber, Stephen Rose
T
Energy Transition Lab, University of Minnesota, United States
ARTICLE INFO
ABSTRACT
Keywords: R&D University-industry Collaborative research Innovation Case studies
Electric utilities have long collaborated with academic researchers, but the lifecycle of these relationships has not been studied systematically. We fill that gap by interviewing utility personnel and quantitatively analyzing papers published by utility-university collaborations. This work suggests best practices for initiating and ensuring the success of utility-university collaborations, while also commenting on the range of collaboration opportunities.
1. Introduction and background Current analysis indicates industrialized countries must rapidly and deeply reduce their emissions of greenhouse gases (GHG) to avoid catastrophic global climate change (IPCC, 2018). The electric power industry has reduced GHG emissions farther and quicker than any other sector, but it faces increasing challenges as it continues decarbonizing. The low marginal cost of renewable energy, along with other factors, is driving the retirement of many fossil-fuel and other thermal power plants that help compensate for renewables’ variability. An increasing portion of new renewable generation comes from small, distributed generators beyond the control of utilities. The electrical grid infrastructure is aging and requires large investments. The electricity industry workforce is also aging: a 2015 survey found roughly 25 % of the electric utility workforce will be ready to retire in the next 5 years (DOE, 2017). Despite these technical and labor challenges, investorowned utilities must find ways to continue to deliver profits to their shareholders and cooperative and municipal utilities must continue to meet the needs of their member-owners amid the changing business environment. This will require innovation and cooperation. Research and development (R&D) is the usual source of innovation, but electric utilities spend significantly less on R&D than firms in other industries (Costello, 2016a; Grubler et al., 2012). Regulation discourages investment in R&D by investor-owned utilities (IOUs) that serve the majority of the electric load in the U.S. As regulated monopolies, they do not face competition and innovations risk devaluing their own assets (Costello 2016a, 2016b). More importantly, these regulated utilities may only recover the cost of investments deemed “prudent” by regulators, which discourages risk-taking (Lyon, 1995; National Academies of Sciences, Engineering, and Medicine, 2016).
⁎
Corresponding author. E-mail address: [email protected] (A. Butts).
https://doi.org/10.1016/j.tej.2019.106680
Available online 29 November 2019 1040-6190/ © 2019 Elsevier Inc. All rights reserved.
Additionally, since allowable profits are tied to the costs of the investment, rather than the benefits, utilities are unable to capture the “upside” of successful investments (Costello, 2016b). However, utilities in competitive, deregulated markets innovate even less than regulated monopolies, preferring to cut costs rather than take risks with long-term investments (Sanyal and Cohen, 2008). Despite those limitations, many utilities previously maintained close, long-term relationships with their local universities to support research and cultivate future employees; however, direct research funding from utilities to universities and indirect funding though the Electric Power Research Institute (EPRI) declined sharply in the 1980s and ‘90s (Russell, 2010). Little research exists on research collaborations between universities and electric utilities. Previous work on these collaborations specifically focuses on technology adoption by utilities, rather than collaborative new technology development (Block et al., 1990; Dedrick et al., 2014; Lester and Hart, 2011; Strong, 2017). Research on utilities’ R&D activities focuses primarily on two aspects: (1) structural and organizational determinants, and (2) factors that affect the success of research collaborations with universities. Larger IOU’s tend to adopt technologies sooner than smaller and publicly-owned utilities (Rose and Joskow, 1990) and are more likely to conduct internal R&D (Sanyal and Cohen, 2008). Utilities describe individual researchers’ specialized expertise and ability to integrate expertise from other fields as universities’ unique strengths (Ware, 1986). However, university researchers familiarize themselves with the details of specific research more slowly than consultants or laboratories, partly because of the cycles of the academic year (Ware, 1986). By contrast, there has been extensive study of university collaborations with non-utility industrial partners. Firms’ primary motivations for collaborating with academics are gaining access to new knowledge and
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
ideas, developing new products, and solving specific problems (Feller et al., 2002; Lee, 2000; Perkmann and Walsh, 2007). Many firms also report a desire to develop and recruit new employees (Feller et al., 2002; Perkmann and Walsh, 2007; Siegel et al., 2003). When firms collaborate with universities, interpersonal relationships are the main conduit for exchanging knowledge (Feller et al., 2002; Oliver and Liebeskind, 1997) and commercializing university-developed technologies ((Siegel et al., 2003). Barnes et al. (2002) find effective projects require matched collaborator interests, appropriate resources, mutual benefits, high-quality project management, and flexibility to accommodate changes. Correspondingly, Siegel et al. (2003) find the most significant barriers are culture clashes, bureaucratic inflexibility, poorly designed reward systems, and ineffective management of university technology transfer offices. University researchers are motivated to collaborate with industrial partners to secure research funding, do research that will gain them profesional recognition, get insights into their own research, and test applications of theory (Lee, 2000; Siegel et al., 2003). Our work refines the research by specifically examining electric utilities to understand why and how they conduct R&D with universities. We begin with a quantitative analysis of publications by joint utilityuniversity teams to reveal patterns in proximity and longevity of their partnerships. We complement the quantitative analysis with qualitative interviews of utility participants in those research projects. These interviews reveal how they find their collaborators, why they collaborate with universities, what factors contribute to success or failure, and where opportunities exist to expand research. This research investigates whether previous findings in other industries apply to the unique context of electric utilities. The answer, largely, is yes. Our findings begin to fill this gap in the literature, and also offer advice for researchers seeking utility collaborations.
2014). The thickness of lines connecting institutions is proportional to the number of shared collaborations. This diagram shows the 17 utilities with 10 or more eligible publications and the top 30 universities, ordered by publication quantity. The “Other” sector lumps together 93 additional universities that have fewer than 10 publications with utilities. Utilities and Universities are ordered (and drawn) from largest to smallest in a clockwise fashion. The transparency of the outer ring differentiates “local” [ < 160 miles] links (opaque), from national links (translucent), and international links (transparent). 2.2. Interviews The qualitative portion of this work is based on semi-structured interviews with seven utility-employed participants (typically engineers or low-level managers) in U-U collaborations. These interviews collect detailed examples and tacit insight into the nature of these collaborations that compliment the quantitative analysis (which excludes collaborations that did not result in publication), but they are not intended to provide a comprehensive overview. Further research should seek to explore other modes of collaboration and a broader range of utility sizes and types. We selected the initial pool of interview candidates as utility personnel who collaborated on several publications in the last 10 years, drawn from the U-U publication dataset. Additional interview candidates were identified by "snowball sampling," i.e. referral by colleagues (Morgan, 2008) and from our own personal networks. We contacted candidates by email and a total of seven people from three investorowned utilities (“IOUs”) agreed to be interviewed, a 20 % success rate. The interviewees we selected worked on various topics: data science, economic modeling, engineering, and business innovation. Interviews were conducted by telephone or in-person, lasted approximately one hour, and were recorded and transcribed. We used a series of open-ended questions designed to identify: (1) types of research well-suited to U-U collaborations, (2) gaps between utility needs and university research (3) unique value of university research to utilities, and (4) reasons why research collaborations either succeed or fail. A complete list of questions is given in Appendix A. We asked interview subjects to first answer the questions about a specific research project they were directly involved in, then asked them to generalize about the representativeness of that experience compared to other projects they participated in or know of (Lee, 2000). We analyze the interviews using a combination of inductive and deductive approaches to qualitative coding. Three broad codes were initially selected to group the different types of data obtained by our list of questions: Project Chronology, Subject of Discussion, and Characteristics of Challenges. Codes were then subdivided into natural categories, i.e. Project Chronology was divided into Initiation, PreProject, During Project, and Post-Project. Open coding was then used within categories to begin conceptualizing the data and identifying thematic connections. Many of the resulting codes were either sub-divided further or reconfigured around a new concept. After reviewing the hierarchy of coding we developed, we then iteratively developed new categories around useful concepts that addressed the key goals of the research in a new hierarchy. All existing codes were able to be resorted into these new categories, which form the framework of our qualitative results sections. A complete codebook of our two coding hierarchies is included in Appendix B.
2. Methods 2.1. Quantitative analysis of publications To generate our initial dataset of University-Utility (U-U) publications, we query the “author affiliation” field in the Scopus database for articles published between 1930 and 2018 that include at least one author affiliated with a large U.S. utility. We select the 20 largest utilities in terms of 2016 revenue listed in Appendix A (T10 in EIA, 2018). The results for each utility include publications under previous company names, by their current subsidiaries or local operating companies, and by utilities they acquired. We exclude several utilities listed in the EIA data that do not appear in the Scopus “Author Affiliations” field (Puget Sound Energy Inc, Long Island Power Authority, and Reliant Energy Retail Services). We then filter the Scopus query results to include only publications that include authors with “University”, “College”, “School” or “Institute” affiliations, which were then manually verified as post-secondary institutions. The publications are further filtered to remove duplicates, including work published under the same title in a conference proceedings and a peer-reviewed journal. For authors who have both university and utility affiliations, we assume the utility is the primary affiliation except in cases when we can determine from the author’s publically-available biographical information that he or she did the research as a student but subsequently submitted or published it while working for a utility. This yields 1020 publications that include both a utility and university author (Butts, 2019). In order to analyze co-authors’ proximity to each other, we assume each author is geographically located at the headquarters of his or her listed institutional affiliation unless some other location is specified. This may mis-identify the locations of employees of local subsidiaries of multi-state utility holding companies. Our analysis of author proximity excludes 17 publications that include authors from multiple universities and multiple utilities. Institutional connections are illustrated by a chord diagram in Fig. 1, created using the “circlize” software package for R (Z. Gu et al.,
3. Results 3.1. How do utilities find academic collaborators? Most collaborations we examine grew from social or professional connections, rather than a deliberate search for a collaborator with specific capabilities. One utility says “it started out [as] just a networking connection between one or two engineers at [the utility] and 2
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
Fig. 1. A chord diagram of publications from collaborations between electric utilities and universities. The thickness of each line represents the number of collaborative publications. Lines of weight 2 or 1 were rendered transparent for overall clarity.
Fig. 2. A (partially stacked) histogram of publication frequency by distance between the closest pair of utility and university collaborators. The first bar is stacked to indicate the portions occurring under 40 miles, and between 40 and 160 miles. 3
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
[a professor], where they had a professional relationship, probably through an IEEE standards group, and talked about ways that they could potentially work together.” (3A) This type of informal brainstorming leading to collaboration was common among our interviewees. We find only one instance of a utility employee seeking unknown researchers with specific expertise, which was achieved through referral via their personal network. The majority of our interviewees describe collaborations with nearby universities. However, our analysis of U-U publications shows a weaker preference for local collaborations. Just half of the 1020 publications include individuals less than 160 miles apart, and only 34 % include individuals less than 40 miles apart. The geographic connection is stronger between institutions: twelve of the seventeen largest U.S. utilities we examine collaborate most frequently with a university within 160 miles, and more than half (9 of 17) collaborate most frequently within 40 miles. The outer ring of Fig. 1 differentiates “local” [ < 160 miles] collaborations (opaque), from national collaborations (translucent), and international collaborations (transparent). Fig. 1 also shows significant differences in diversity of utilities’ institutional collaborators. Interestingly, our quantitative analysis shows frequent collaborators (≥5 publications together) are less likely to be near each other than UU collaborators in general. Only 29 % of these prolific pairs are within 160 miles of one another. One interviewee with repeat collaborations explains: “[Nearby U.] might as well be [Distant U.]. I'm not going up to [Nearby U.] 25 miles away. With computers and email, it's as easy to work with someone in [Distant U.] as it is [Nearby U.] for me.” (3B) The fact that single-publication collaborations are more common for nearby collaborators (Fig. 2) suggests that although shorter distances may make starting collaborations easier, distance does not seem integral to the endurance of repeat-collaborations between individuals. Alternatively, the stronger connection between local institutions than individuals may suggest that university researchers instead collaborate with several different people at a nearby utility. This is consistent with several interviewees, who describe introducing academic collaborators to other departments within their utility after their collaboration ends. The publication-author data we analyze suggests the number of publications and number of authors and organizations per publication are increasing over time. Future research could augment our data (Butts, 2019) with data on R&D funding and changes in grant evaluation criteria that may help explain or control for these observed trends. Our publication data also shows the distances between collaborators is increasing over time. Future research could test hypotheses to explain these trends, including examining whether this reflects the persistence of collaborations as researchers transfer to different universities.
cases we examine, the university approached the utility first. Only one interviewee describes the opposite: deliberately seeking academic researchers with specific capabilities. In that case, the interviewee wanted the study to be done by an academic institution, which regulators would find more credible than a consultant. Most interviewees also describe wanting to contribute to the “pipeline” of new skilled workers. Interestingly, they describe wanting to cultivate talent not just for their own utility, but for the broader utility industry. One says “[We’re] hoping and expecting that [these students] come into industry, maybe not directly to work for us but they're strengthening the greater industry” (3A) and another explains “those are the people that are going to bubble up in the field and become the next generation of researchers and consultants” (2A). 3.3. What makes collaborations succeed or fail? Interviewees consider collaborations most successful when they meet a business need for their utility. As described above, interviewees typically match the capabilities of researchers who approached them to utility business needs, rather than deliberately seeking researchers with specific capabilities. Interviewees prefer researchers who approach them offering capabilities, which are easier to match than projects. One interviewee likes a professor’s initial pitch, which he/she remembers as “These are our skills. Share with us your areas of interest needs and pain points” (1A). Another says he/she found a successful match “not [by] taking the specific [project] recommendations of the university group” but by finding a group within the utility that needed their “skills in managing big data sets and building models to extract information from them” (1C). In contrast, utilities are less enthusiastic about being approached with a researcher’s favored project or approach: “Utilities are not starved for ideas. People [are always] knocking at the door, saying ‘Hey, I’ve got this great widget’,” (1A) while another opines, “They have this vision they want to sell us, but very often it doesn't align with what we actually need.” (2A) Most interviewees also describe being approached by researchers requesting data, and suggest they would be more receptive to those requests if they meet a business need. One says, “If [academics] want data from the utility, they should come and ask 'What can we do for you?', in addition to what they want to do in their own research track.” (2A) While meeting a business need is regarded as critical to success, there was no consensus whether university collaborations are better suited to narrowly-defined projects or more open-ended research. Many interviewees describe successes and failures with both kinds of research, but agree that consultants are ill-suited to open-ended work. Interviewees with an academic affinity are more optimistic about openended research, and praise the unexpected insights that can arise. One explains: “…the most impactful research findings are not those that you would anticipate at the outset. You need to be able to follow your nose a little bit.” (2A) Reading into the context of open-ended research disappointments, most seem to involve inexperienced student workers or poor project management. This is unsurprising, as project management skills were identified as critical to any collaboration. Legal negotiations pose a substantial barrier to U-U collaborations, though no interviewees described them as insurmountable. The negotiations are often time-consuming because they must reconcile utilities’ and universities’ standard “boilerplate” contracts. In some cases, timeconsuming negotiations have derailed timing-sensitive projects. If the start of a project is delayed, the continuity of student researchers may be disrupted by graduation or professors may become distracted by teaching responsibilities. Additionally, the significant negotiating time and effort may deter a potential utility collaborator, especially if they don’t see a compelling business case. Even legal issues that don’t require negotiation can be a barrier to collaboration. Nearly every interviewee specifically mentions non-disclosure agreements (NDA), data security protocols, and a concern that university researchers are unfamiliar with or disinterested in them.
3.2. What motivates utilities to collaborate with university researchers? We find most of our interviewees are motivated, in part, to collaborate with universities by what we term affinity for academic research. This usually stems from their prior experience doing research at a university (often as a graduate student), or from positive experiences collaborating with universities. An interviewee and former postdoctoral researcher says “I wanted to help… I would love to see a day where we have a research objective and instead of going to a consultant, we go to a university.” (2A). Personal affinity for academic research is significant because a utility “is made up of individuals and those individuals will make decisions about whether or not they strike up a collaboration with a consultant or a university or a national lab." (2A) This affinity reveals itself through interviewees who found convenient matches between their business needs and the capabilities of researchers they or their coworkers already knew rather than deliberately searching for researchers with specific capabilities. An interviewee says “I can almost assure you [utility] did not reach out and scan a bunch of academic institutions to try to figure out who to go to with all of these data … That's not our model. Our model, if we need to do something like that, is to hire a consultant, not an academic institution.” (2A). In half the 4
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
3.4. Project management
ended types (data science, business innovation, policy modeling), are well-suited to universities and expected to thrive in the current industry climate. This work is also the first quantitative analysis of universityutility collaborations, and it shows that local collaborations more often produce one-off collaborations but perennial collaborations tend to flourish from favorable conditions beyond mere proximity. The quantitative and qualitative analyses in this work complement each other, painting a more complete picture of utility-university collaborations than either method alone. Our quantitative results reveal that repeat collaborators tend to be farther apart than average, while our interviews reveal that geographic proximity is less important than good project management and meeting a utility business need. Future research could use the quantitative data we compiled (Butts, 2019) to analyze the effect of those factors on the number of publications using network analysis of connections between co-authors. However, interviewees describe types of research collaborations that do not appear in our quantitative dataset (failed projects, projects without publications, practicum projects, and consulting), which indicates analyzing publications under-estimates collaborations between universities and utilities. They also talk about non-research interactions between utilities and universities, such as giving guest lectures or teaching courses, giving strategic advice, and participating in regulatory proceedings. Finally, interviewees mention topics that rarely appear in the publication record, such as new business models for utilities, which suggests an unmet need for academic research. For university researchers interested in working with electric utilities, our research offers several insights. First, it is often fruitful to just ask. Invite utility personnel for a lab tour or offer to give a presentation. Academic researchers typically meet them through social or professional networks, and find that utility personnel who have done academic research themselves tend to be more receptive to collaborating. “Boundary organizations” that straddle the boundaries between academia and utilities and between academic disciplines can also facilitate those connections (Guston, 2001). Our research was initiated to inform the strategic direction of one such boundary organization, the Institute on the Environment at the University of Minnesota. Second, when discussions begin, emphasize your research capabilities rather than proposing a particular project. The initial connection may not necessarily lead to a collaboration, but that person may be able to connect you to someone else in the utility who needs those capabilities. Finally, utilities highly value researchers who effectively manage projects. Communicate regularly with the utility project sponsor to address problems early and ensure the project stays on-schedule. Utilities face great challenges that academic research can help address and universities seek research topics, funding, and data that utilities may be able to provide. More broadly, universities have a mission to apply their knowledge and expertise to real-world problems. The electric power system and the electricity industry are changing rapidly and in unprecedented ways. The IPCC predicts global GHG emissions must fall 45 % by 2030 and reach “net zero” by 2050 to limit global warming to 1.5 °C and mitigate climate catastrophe (IPCC, 2018). Countries such as Germany, U.S. states such as California, and U.S. electric utilities such as Xcel Energy have adopted policies to achieve those goals. That will require radical changes to electrical generation and widespread electrification of transportation and heating, which puts electric utilities at the forefront of this change. Academic researchers can assist electric utilities in this transformation. Our research finds that utilities see many topics on which they can collaborate with universities collaborations, and many utility employees welcome researchers who introduce themselves, describe what they can do, and ask, “What can we do for you?”
Many interviewees describe academics as being inexperienced with project management, although they admit utilities could also improve. One says “those people probably are fairly guaranteed to be super smart and capable, but are they going to get your project done or not?” (1C). Several speculate about their own PM missteps, wondering if prior projects would have gone better: “Maybe if we… were a bit more proactive in managing … and keeping the project schedule on track…” (1C). But successful repeat collaborators often share project management responsibilities across their respective organizations. The utility personnel we interview identify three project management functions that are particularly important to the success of research collaboration: Consistent Communication, Connecting Resources, and Timeline Management. Consistent communication is important to identify problems early and ensure the work doesn’t lose sight of business needs. Connecting collaborators to resources, such as utility data and subject matter experts in other fields or departments, helps overcome the “siloed” nature of universities and utilities. Managing schedules and the project timeline is crucial because business processes and academic calendars operate on incongruous cycles. Whether vacations, graduations, or competitive bidding requirements, interviewees observed setbacks often snowballed if slippage wasn’t properly communicated and managed. 3.5. Opportunity areas for university-utility collaborations Our interviewees describe several challenges well-suited to universities. Most say they need help analyzing the “tidal wave” (2A) of data they collect because “utilities aren't typically very well staffed with [data scientists] and they're expensive … ‘big data’ capabilities really fills a gap for us.” (1C) Several also look for help adapting to “evolving regulatory changes” and finding “future business models and revenue making opportunities” (1A). Utilities’ regulatory environments make these innovations difficult to test, but one interviewee describes how regulators created a “regulatory sandbox… to demonstrate and test the new business models.” (1A) The majority of U-U research is published in engineering journals (> 57 %). Accordingly, several interviewees mention U-U engineering projects involving simulation, modeling, or specialized hardware testing; which helps “de-risk” designs before piloting hardware on the actual grid. (1A) Hardware research can be easier to manage compared to less tangible research because the deliverables are easier to define and measure, but university-designed solutions often prove difficult to commercialize at scale. One interviewee states “universities are not in the business of manufacturing and selling you anything. … I want to buy it from somebody that could warranty it and service it… and it’s not just one or two… I need like a hundred” (1A). 4. Conclusions and discussion Faced with a rapidly evolving business and regulatory environment, universities can help utilities adapt to the uncertain future by addressing needs in the present. This is the first investigation into the lifecycle of research collaborations between electric utilities and universities: how and why they begin and what fosters success or failure. We find collaborations typically sprout from social connections. It is often academics who plant the seed of a collaboration; they introduce their capabilities by, for example, giving a presentation at a utility or inviting them for a lab tour. Potential collaborations find fertile ground in utility personnel who have an affinity for academic research and a desire to cultivate budding engineers and analysts for the industry. Meeting a utility’s business need, more than intellectual cleverness, will nurture a collaboration’s success. Effective project management, particularly consistent communication, ensures collaborations bear the desired fruit. Many varieties of research, including intangible (simulation, testing) and open-
Acknowledgements This research was supported by the Institute on the Environment, University of Minnesota, United States, DG-0002-17. 5
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
Appendix A Queries of Scopus database to find utility-university publications We query the “author affiliation” field in the Scopus database for articles published between 1930 and 2018 that include at least one author affiliated with a large U.S. utility. This is 20 separate queries– one for each utility. We select the 20 largest utilities in terms of 2016 revenue, listed in the table below. We exclude several utilities listed in the EIA data that do not appear in the Scopus “Author Affiliations” field (Puget Sound Energy Inc, Long Island Power Authority, and Reliant Energy Retail Services) and we combined utilities that are listed separately in the EIA data but are owned by larger holding companies (e.g. Duke Energy) or have merged (e.g. Public Service Company Colorado and Northern States Power Company). We then filter the combined Scopus query results to include only publications that include at least one author with “University”, “College”, “School” or “Institute” in the name of his or her affiliation. University-affiliated research institutes were counted as universities, independent research institutes such as EPRI were not. This yielded 1296 publications, available at (Butts, 2019). Alabama Power Co Union Electric Co - (MO) (Amren) Arizona Public Service Co Commonwealth Edison Co (Commonwealth Edison Co; ComEd) Consolidated Edison Co-NY Inc Consumers Energy Co DTE Electric Company Duke Energy (Duke Energy Carolinas; Duke Energy Progress; Duke Energy Florida; Duke Energy Indiana) Entergy Louisiana LLC (Entergy Corporation) Georgia Power Co Pacific Gas & Electric Co
PacifiCorp Xcel Energy (Public Service Co of Colorado) Public Service Elec & Gas Co (Public Service Enterprise Group Inc) Salt River Project South Carolina Electric & Gas Company (SCANA) Southern California Edison Co TXU Energy Retail Co LP Virginia Electric & Power Co (Dominion Virginia Power Co) Wisconsin Electric Power Co Northern States Power Co - Minnesota (Xcel Energy)
We use a computer script to parse the text of the author affiliations into fields for each individual author, the institutions with which they are affiliated, and the locations of those institutions. We then manually cleaned the data to fix incorrectly-parsed affiliations. When possible, we found affiliations missing from the Scopus database in the text of published version of the paper. In cases where a single author listed an affiliation with both a utility and university, we used the author’s publicly-available biographical information (typically a resume/CV) to infer which affiliation was their primary. If a publication was submitted when the author was affiliated with a university (typically as a student), we used the university affiliation. For other multiple-affiliation authors, we used the author’s listed address to determine primary affiliation. When that was not available, we assumed the author’s primary affiliation was the utility and that their university affiliation was something akin to “adjunct”. This excluded 216 publications, yielding 1080, available at (Butts, 2019). Finally, we excluded 60 duplicate publications that reflect both a paper and a conference presentation, even in the cases where the names were not a perfect match. This yields 1020 unique publications that include both utility and university authors, available at (Butts, 2019). We calculated distances between collaborators based on their listed addresses or, when those were not available, the addresses of their primary affiliations. This may mis-locate some authors, especially employees of utility holding companies that span several states. For publications with multiple collaborating institutions, we used the minimum distance between a university and a utility. The 17 publications from multiple utilities and multiple universities were excluded. Appendix B Interview questions We asked interviewees to focus their answers on a specific collaboration in which they had been involved. Later we asked them to generalize their experience and compare it to other collaborations in which they had been involved. We asked the following questions in the semi-structured interviews: 1 2 3 4 5 6 7 8 9 10
How did this relationship begin and how did it evolve into an actual research collaboration? How did the relationship evolve into a research project? What conditions were necessary? What barriers/challenges did you face? What challenges or problems arose in this project? If the project finished, what relationship do you now have this university? How does your experience collaborating with academic researchers compare to collaborations with other research institutes, government agencies, or consultants? What are the advantages and disadvantages of working with a university? Do have any advice or a utility employee considering a collaboration with a university? What kinds of problems utility problems do you think are well suited to collaboration with universities? Are there any questions I should have asked? Is there anything you want other people (especially, but not limited to, academic researchers) to know? Qualitative Codes for analyzing interviews We coded passages of the transcribed interviews with the following codes: Initial codes
1 Miscellaneous a Interesting But Unclassified b Memorable Quotes 2 Project Chronology 6
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
3 4 5 6
7
8
a Deliverables and Post Project i Follow-on Relationship 1 Continued 2 Ended 3 Expanded ii Success Level 1 Failure 2 Partial 3 Total b During Project i Communication ii Continuity 1 Individual Continuity 2 Skill Continuity iii Hurdles 1 Eased or Facilitated 2 Impeded or Slowed iv Project Management v Shift in Expectations vi Timeline c Initiation i Initiator 1 University Initiated 2 Utility Initiated ii Motivation of Initiation 1 Advantages of academic research Labor New knowledge Other advantage Specific capabilities 1 1 1 Attitude toward academic research 2 Workforce development pipeline 2 Type of Introduction 1 Exploratory Introduction 2 Social Introduction 2 Pre-Project i Business Process 1 Schedule or Timeline ii Definition of Contributions of Each Party 1 Connections 2 Equipment 3 Funding 4 Knowledge 5 Motivation 6 Prior Experience 7 Skills iii Definition of Need-Deliverables iv Legal Stuff 1 Data Sharing Agreements 2 NDA 3 Ownership of Results Subject of Discussion a Non-University i Company ii Government Body iii Research Organization b University c Utility Utility Challenges Amenable to University Research a Characteristics i Risk 1 Interfering 2 Non-Interfering ii Technical Maturity 7
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
1 Applied 2 Early Stage 3 Implementation b Topics i Hardware and Testing ii Modeling or Simulation iii Process iv Public Policy Second-round codes 1. Introduction & Background a. University Collaborations with Industry b. What's special about utilities 2. What Motivates Utilities to Collaborate? a. Advantages of Collaborating with Universities b. Personal Receptiveness or Attitude Towards University Research c. Pipeline for New Employees 3. How do utilities find collaborators? a. Evidence for Local Collaborations b. Stories about how they met c. Evidence for Connection to Alma Mater d. Evidence for Repeat Collaborations 4. What impedes a prospective collaboration? a. Legal Negotiations i. Legal Overview ii. Data Sharing iii. Data Security iv. IP or Commercialization b. Business Processes i. Procurement ii. Timeline iii. Other 5. What Makes Collaborations Succeed or Fail? A. Matching i. Matching – Interests,_Capabilities,_Needs ii. Capabilities a. Kid Gloves b. Technical iii. Interests a. Incentives – Continuity iv. Needs v. Financial Arrangements vi. Meeting a Need B. Project Definition i. Deliverable Definition ii. Flexibility or Improvisation iii. Publication C. Project Management i. Accountable Support ii. Project Management iii. Communication iv. Right People - SME's D. Opportunity Areas i. Qualities of collaborations ii. De-risking iii. Commercialization iv. Engineering and Hardware v. Modeling, Data, and Business 6. Conclusions and Discussion a. Advice for Professors b. Advice for Universities c. Real vs. Perceived Problems d. Potential Questions for a follow-on survey
8
The Electricity Journal 32 (2019) 106680
A. Butts, et al.
References
National Academies of Sciences, Engineering, and Medicine, 2016. The Power of Change. National Academies Press, Washington, DC. https://doi.org/10.17226/21712. Oliver, A.L., Liebeskind, J.P., 1997. Three levels of networking for sourcing intellectual capital in biotechnology: implications for studying interorganizational networks. Int. Stud. Manag. Organ. 27 (4), 76–103. https://doi.org/10.2307/40397388. Perkmann, M., Walsh, K., 2007. University–industry relationships and open innovation: towards a research agenda. Int. J. Manag. Rev. 9 (4), 259–280. https://doi.org/10. 1111/j.1468-2370.2007.00225.x. Rose, N.L., Joskow, P.L., 1990. The diffusion of new technologies: evidence from the electric utility industry. Rand J. Econ. 21 (3), 354. https://doi.org/10.2307/ 2555614. Russell, B.D., 2010. Educating the workforce for the modern electric power system. Bridge 40 (1), 35–41. Sanyal, P., Cohen, L.R., 2008. Powering progress: restructuring, competition, and R&D in the U.S. Electric utility industry. Electr. J. 30 (2), 41–79. https://doi.org/10.2307/ 41323233. Siegel, D.S., Waldman, D.A., Atwater, L.E., Link, A.N., 2003. Commercial knowledge transfers from universities to firms: improving the effectiveness of university–industry collaboration. J. High Technol. Manag. Res. 14 (1), 111–133. https://doi.org/10.1016/S1047-8310(03)00007-5. Strong, D.R., 2017. Determinants of early smart meter diffusion in the US electric power industry. Ssrn Electron. J. https://doi.org/10.2139/ssrn.2942960. Ware, B.J., 1986. High voltage engineering - electric Utility/University research interactions in the USA. Power Syst. IEEE Trans. 1 (4), 207–210. https://doi.org/10.1109/ TPWRS.1986.4335045.
Barnes, T., Pashby, I., Gibbons, A., 2002. Effective university – industry interaction: a multi-case evaluation of collaborative R&D projects. Eur. Manag. J. 20 (3), 272–285. Block, R.B., Beardsworth, E., Chan, C., 1990. An evolving technology-transfer system in an R&D consortium: the EPRI approach. J. Technol. Transf. 15 (1-2), 17–24. https:// doi.org/10.1007/BF02377653. Butts, A., 2019. Utility-University Collaboration Publication Data. Mendeley Data. https://doi.org/10.17632/87y67tnxtf.1. Costello, K.W., 2016a. A Primer on R&D in the Energy Utility Sector (No. 16-05). National Regulatory Research Institute, Silver Spring, MD, pp. 1–52. Costello, K.W., 2016b. R&D and public utilities. Electr. J. 29 (5), 19–26. https://doi.org/ 10.1016/j.tej.2016.06.001. Dedrick, J., Venkatesh, M., Stanton, J.M., Zheng, Y., Ramnarine-Rieks, A., 2014. Adoption of smart grid technologies by electric utilities: factors influencing organizational innovation in a regulated environment. Electron. Mark. 25 (1), 17–29. https://doi.org/ 10.1007/s12525-014-0166-6. DOE, 2017. The Electricity Workforce: Changing Needs, New Opportunities. In Transforming the Nations Electricity System The Second Installment of the Quadrennial Energy Review. energy.gov. EIA, 2018. Annual Energy Outlook 2018. US Energy Information Administration., Washington, DC, pp. 1–74. Feller, I., Ailes, C.P., Roessner, J.D., 2002. Impacts of research universities on technological innovation in industry: evidence from engineering research centers. Res. Policy 31 (3), 457–474. https://doi.org/10.1016/S0048-7333(01)00119-6. Grubler, A., Aguayo, F., Gallagher, K., Hekkert, M., Jiang, K., Mytelka, L., et al., 2012. Policies for the energy technology innovation system (ETIS). In: Johansson, T.B., Nakicenovic, N., Patwardhan, A., Gomez-Echeverri, L. (Eds.), Global Energy Assessment (GEA). Cambridge University Press, Cambridge, pp. 1665–1744. https:// doi.org/10.1017/CBO9780511793677.030. Gu, Z., Gu, L., Eils, R., Schlesner, M., Brors, B., 2014. Circlize implements and enhances circular visualization in R. Bioinformatics 30 (19), 2811–2812. https://doi.org/10. 1093/bioinformatics/btu393. Guston, D.H., 2001. Boundary Organizations in Environmental Policy and Science: An Introduction. Science, Technology, & Human Values 26 (4), 399–408. https://doi. org/10.1177/016224390102600401. IPCC, et al., 2018. In: Masson-Delmotte, V., Zhai, P., Pörtner, H.O., Roberts, D., Skea, J., Shukla, P.R. (Eds.), Global Warming of 1.5°C, Geneva. Lee, Y.S., 2000. The sustainability of university-industry research collaboration: an empirical assessment. J. Technol. Transf. 25 (2), 111–133. https://doi.org/10.1023/ A:1007895322042. Lester, R.K., Hart, D.M., 2011. Unlocking Energy Innovation. MIT Press. Lyon, T.P., 1995. Regulatory hindsight review and innovation by electric utilities. J. Regul. Econ. 7 (3), 233–254. https://doi.org/10.1007/BF01067096. Morgan, D.L., 2008. Snowball sampling. In: Given, L.M. (Ed.), The SAGE Encyclopedia of Qualitative Research Methods. SAGE Publications, Inc, Thousand Oaks. https://doi. org/10.4135/9781412963909.n425. p. 816.
Andrew Butts “Andrew Butts is a graduate research assistant at the Institute on the Environment and attends the Humphrey School of Public Affairs at the University of Minnesota while pursuing his Master of Science, Technology, and Environmental Policy. He previously earned his BBA from UW-Madison, studying Operations Management and Communication Arts. He is a core organizer for the Twin Cities’ Science for the People chapter. He hopes to integrate his skills in analysis, communications, and policy to engage the public on society’s greatest challenges." Julia Wilber “Julia Wilber is a graduate research assistant at the Institute on the Environment and attends the Humphrey School of Public Affairs at the University of Minnesota while pursuing a dual Master of Public Policy and Master of Social Work, with a focus on Human Rights and Mental Health. She previously earned her BA from Hamilton College, with an Interdisciplinary Concentration on Social Justice, Peace, and Development." Stephen Rose “Stephen Rose is a postdoctoral researcher at the Institute on the Environment and the Humphrey School of Public Affairs at the University of Minnesota, investigating long-term investment planning by electric utilities. He earned a Ph.D. in Engineering & Public Policy from Carnegie Mellon University and previously designed control systems for wind turbines at G.E."
9