Open innovation in the power & energy sector: Bringing together government policies, companies’ interests, and academic essence

Abstract

The Power and Energy (P&E) sector needs to respond to several challenges fostering investments in research and development. According to the Open Innovation (OI) paradigm, key stakeholders like utilities, vendors, laboratories, universities etc. should take advantage of external knowledge to improve their innovation performance. Several studies have demonstrated that firms adopting the OI paradigm are more likely to innovate. Despite the interest of P&E firms in enhancing their innovation capabilities, surprisingly few articles (usually case studies) described the implementation of the OI paradigm in P&E firms. This article fills the gap by identifying the key drivers that encourage a firm in the P&E sector to embrace the OI paradigm. The authors adopt a hybrid research approach collecting evidence from the literature and through a multiple case-study analysis involving seven British firms and universities operating in the P&E industry. As the drivers of OI have mutual influence, this article describes them with a fuzzy cognitive map. Finally, the authors identify appropriated policies to enhance the OI adoption and, consequently, the sustainability of innovation in the P&E sector. A salient research agenda closes the paper.

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

“…Besides, fuzziness (UT2) and subjectiveness (UT4). The remaining two types of uncertainty, UT1-Random uncertainty (e.g., [183,214]) and UT3-Grey uncertainty (e.g., [19,88]) are less treated by researchers in strategic EPDM (11% and 16%, respectively). Therefore, in most case studies, the uncertainties have been handled throughout (1) fuzzy linguistic (38%) or (2) fuzzy (23%) decision-making environments while considering other types of preferences' representation (e.g., intervals [199,215], intuitionistic [111,197], or trapezoidal fuzzy numbers [41]).…”

“…(3) Last, this period has been the most remarkable wherein 6 papers are identified [81,109,170,181,203,214]. Firstly, Maté et al [181] explored the opportunities to adopt more intelligent ways of managing existing RES.…”

“…Ghosh et al [203] developed an integrated decision-making method that combines ANN and MCDM techniques to predict an index that directly represents the suitability of locations for wave energy generation. Greco et al [214] suggested to integrate the open innovation paradigm (OIP) in the energy sector to take advantage of external knowledge. The authors [214] stated that this paradigm will certainly help key stakeholders (e.g., utilities, vendors, laboratories, and universities) to improve their innovation performance.…”

“…The remaining articles address uncertainty (mostly in an MCDM context) by carrying out sensitivity analysis (26%), monte carlo simulation (5%), or scenario analysis (3%) of the criteria weighting as a way to check the robustness of the results. Additionally, (fuzzy) cognitive maps are apparently the most used to handle uncertainty due to causal relationships (i.e., UT1) [202,214]. Last, Table 6 reports the use of some other techniques (11%)-as exceptions -to handle some particular situations of energy planning under uncertainty such as interval linear programming [22], cloud theory [114], objective criteria [67], datamining [181], BNs [109], factor screening method [208], real options valuation [210], and risk-aversion optimization [183].…”

Collaborate to innovate

“…Open innovation tools include online innovation challenges and smart IP management, where the knowledge peripheral to the main protected product is more openly shared. While the open innovation approach has yet to be more fully explored in the energy sector, and its potential benefits or drawbacks have yet to be analysed, a recent study identified likely drivers of open innovation adoption in the energy sector 5 . These drivers could include economic slow-down and risk aversion.…”