Of the components and processes included in utility-scale PV balance of systems, inverters and tracking systems are best suited to the analytical methods that we consider in Knowledge Conditions. However, we note that, given the relevant search terms and patent codes, similar studies could be performed on other BOS components like monitoring systems, for example.
Knowledge as a Resource – Absorptive Capacity
Absorptive capacity (ACAP) refers to the conditions that allow firms to incorporate outside innovations. A Popular metric of a firm’s ACAP is research and development (R&D) input (e.g., the amount of money or share of revenue a firm devotes to R&D). Beyond simply increasing R&D input, ACAP also depends on whether or not a firm has continuously invested in R&D. Employee education level (i.e., share of employees with higher education in total employee) is associated with ACAP and diversity of backgrounds among R&D personnel can help an organization maximize “novel associations and linkages” (Taylor, Fujita, & Dale, 2013). Organizational structure and knowledge management culture within a firm, as well as the type of knowledge to be learned externally (e.g., intra- versus inter-industry; science-based; private-to-public or public-to-private sector) also impact a firm’s ACAP (Schmidt, 2010). Hoppmann (Hoppmann, 2016) demonstrated that, in the solar PV industry, investment in standardized manufacturing equipment, in addition to firm’s investments in R&D activities provides a strong metric of absorptive capacity. Research suggests that improvements in the cost-to-performance ratio of PV technologies have been driven primarily by inter-firm knowledge spillovers.
R&D Input
Generally, a firm’s R&D input has a strong correlation to the number of patents it generates. Figure KC.1 shows global R&D input to renewable energy over time, which increased through 2009 and then flattened. In the US in 2016, 75% of solar R&D spending ($300 million) came from private corporations, while the remaining 25% was supported by government funding (Table KC.1); this R&D included photovoltaic, concentrated solar power, and solar thermal technologies (McCrone et al.2017). Compared to other renewable technologies, solar R&D is funded much more from corporate companies than government in the US (see Figure KC.2; see Figure KC.3 to compare US solar R&D to other countries). Braun et al. (2010) note that public R&D is particularly effective at stimulating innovations in solar technologies. In publications tracking R&D spending, “solar energy” or “renewable energy” technologies are often discussed in aggregate, making it difficult to focus on a specific component such as inverters and other parts of utility solar BOS. However, R&D input focusing on improvements to inverters is likely to scale with general R&D for PV (and renewables generally).
Figure KC.1 Global R&D Investment in Renewable Energy, 2004-2016, $BN
Source: Figure 54 from Global Trends in Renewable Energy Investment 2017 (McCrone et al. 2017), original data from Bloomberg, Bloomberg New Energy Finance, IEA, IMAF, various government agencies
Table KC.1. Renewable Energy Investment in the US by Sector and Type, 2016, $BN
Source: Recreated from Figure 15 in Global Trends in Renewable Energy Investment 2017 (McCrone et al. 2017), original data from UN Environmental , Bloomberg New Energy Finance
| Energy Source | Asset finance | Reinvested equity | SDC | Public markets | VC/PE | Corporate R&D | Government R&D | Total |
| Solar | 14.7 | -0.8 | 13.1 | 0.2 | 1.7 | 0.3 | 0.1 | 29.3 |
| Wind | 14.7 | -0.6 | — | 1.1 | 0.2 | 0.0 | 0.1 | 15.5 |
| Biofuels | 0.1 | — | — | 0.0 | 0.3 | 0.1 | 0.6 | 1.0 |
| Geothermal | — | — | — | — | — | 0.0 | 0.1 | 0.1 |
| Biomass & waste | 0.2 | — | — | — | 0.1 | 0.0 | 0.1 | 0.4 |
| Small hydro | 0.1 | — | — | — | 0.0 | 0.0 | 0.0 | 0.1 |
| Marine | — | — | — | — | 0.0 | 0.0 | 0.0 | 0.1 |
| Total | 29.8 | -1.5 | 13.1 | 0.0 | 2.3 | 0.5 | 1.0 | 46.4 |
SDC: Small Distributed Capacity. Total values include estimates for undisclosed deals; VC/PE: Venture capital and private equity.
Figure KC.2 Renewable Energy R&D Global Investment by Technology Type
Source: Figure 55 from Global Trends in Renewable Energy Investment 2017 (McCrone et al. 2017), original data from Bloomberg, Bloomberg New Energy Finance, IEA, IMAF, various government agencies
Figure KC.3 Renewable Energy R&D Global Investment by Region
Source: Figure 56 from Global Trends in Renewable Energy Investment 2017 (McCrone et al. 2017), original data from Bloomberg, Bloomberg New Energy Finance, IEA, IMAF, various government agencies
Knowledge Creation
Patent Quantity
Using the number of patents generated as a proxy for knowledge creation is an imperfect yet useful way to measure innovation. Patent counts are a particularly helpful metric of pre-commercial innovation output. Other similar indicators include publications, licenses, and tacit knowledge “know-how”. The PV industry is highly globalized, and accordingly, PV knowledge creation and spillover are also geographically dispersed. Because not all inventions are patentable and because patenting is not always perceived by firms as the most efficient and effective protection tool, the propensity to patent differs across firms, industries and the type of innovation. Firms must first exhibit innovation capabilities necessary to producing patentable knowledge and then have willingness to patent (Huang & Cheng, 2015).
Knowledge relevant to PV technology is generated from diverse disciplines. Most energy innovations are not developed specifically by energy companies; instead, they enter the sector embodied in specialized equipment or innovative materials from other sectors. For example, solar panels rely heavily on developments in semiconductors (Huenteler, Schmidt, Ossenbrink, & Hoffmann, 2016). PV patents can be categorized by value chain segment; for instance, Binz et al ( 2017) provided a set of IPC codes based on value chain segments defined as follows: Upstream – silicon casting, turnkey manufacturing lines; Core – cell, module, manufacturing; Downstream – balance of system, after-sales services, energy storage (Figure KC.4). Table KC.2 describes several IPC codes related to solar inverters, while Table KC.3 presents the number of patent applications by company for one particular IPC (H02S40/32). Information on inverter and other BOS component patents can be found in numerous patent search databases including the United States Patent and Trademark Office (USPTO), the European Patent Office (EPO), Derwent World Patents Index, World Intellectual Property Organization, and Google Patents, etc.
Figure KC.4 Global Annual 1st Generation Solar PV Patent Counts
Source: Top panel of Figure 6 from (Binz et al., 2017)
Note: Upstream: silicon, silicon doping, ingots, and wafer sawing; Core: cell, module manufacturing; Downstream: inverters, batteries, grid integration, rooftop covers, consumer products.
Table KC.2 Patent Codes Relevant to Inverters
Source: http://www.wipo.int/classifications/ipc
Table KC.2 Patent Codes Relevant to Inverters
Source: http://www.wipo.int/classifications/ipc
| Code | Description |
| H | Electricity |
| H02 | Generation; conversion or distribution of electric power |
| H02M | Apparatus for conversion between AC and AC, between AC and DC, or between DC and DC, and for use with mains or similar power supply systems; conversion of DC or AC input power into surge output power; control or regulation thereof |
| H02S40/32 | Electrical components comprising DC/AC inverter means associated with the PV module itself, e.g. AC modules |
| H02J3/383 | Solar energy, e.g., photovoltaic energy |
Table KC.3 Applications for Patent Class H02S40/32
Source: http://www.patbase.com
| Assignee | Applications |
| SUNPOWER CORP | 88 |
| LG ELECTRONICS INC | 74 |
| SUNGROW POWER SUPPLY CO LTD | 57 |
| SOLARLYTICS INC | 50 |
| LSIS CO LTD | 32 |
| ENPHASE ENERGY INC | 21 |
| SMA SOLAR TECHNOLOGY AG | 14 |
| HUAWEI TECHNOLOGIES CO LTD | 11 |
| BUILDING MATERIALS INVESTMENT CORP | 11 |
| ALTENERGY POWER SYSTEM INC | 11 |
| GENERAL ELECTRIC CO | 10 |
| TENKSOLAR INC | 8 |
| SOLPAD INC | 8 |
| KYOCERA CORP | 8 |
| ADVANCED ENERGY IND INC | 8 |
| SOLARCITY CORP | 7 |
| OMRON TATEISI ELECTRONICS CO | 7 |
| LA VECCHIA NUNZIO | 7 |
| CHINA MINSHENG INVEST CORP LTD | 7 |
| AU OPTRONICS CORP | 7 |
Figures KC.5 and KC.6 illustrate the knowledge landscape over time of PV technology in general, as compiled from many sources of patents. Figure KC.5 represents solar energy patent evolution in the US, while Figure KC.6 presents a global overview of recent solar energy patents by country. Note that these results represent a general summary and trend in knowledge stock, and do not reflect the nuance of the search terms, data sources, and scope defined by each of the individual studies. Finally, Figure KC.7 presents a breakdown of patents by country based on applications for IPC class H02S40/32.
Figure KC.5 Accumulated Patent Evolution of PV Technology in the US
Source: (IRENA, 2017). Note: The information in this search utilizes data from EPO PATSTAT and uses the Climate Change Mitigation Technologies (Y02) classification by EPO.
Figure KC.6 Global Overview on PV Patents (accumulated patent counts between 2000 and 2013)
Source: (IRENA, 2017). Note: The information in this search utilizes data from EPO PATSTAT and uses the Climate Change Mitigation Technologies (Y02) classification by EPO.
Figure KC.7 Patenting Activity by Country for IPC Class H02S40/32
Source: http://www.patbase.com
Historically, PV technology knowledge was created by pioneer innovators in the US and Japan; today, US patents represent a lower share of the global PV patents compared to 20-30 years ago, but retain higher quality measures throughout the lifecycle in all PV value chain segments. Previous studies (e.g., Binz et al., 2017) demonstrated that, historically, PV knowledge creation and technology innovation dynamics remained largely unaffected by globalization of the PV market and relocation of primary manufacturing sites (see Figure KC.8). This implies that creating basic R&D driven inventions and pioneering companies in an emerging industry is by no means a guarantee that significant manufacturing volumes and jobs will be retained in the same country in later industry lifecycle phases.
Figure KC.8 Country Shares in Global 1st Generation Solar PV Patents
Source: Second panel of Figure 6 from (Binz et al., 2017)
Bibliometric Analysis
Bibliometric analysis applies quantitative and statistical methods to study trends in the quantity and quality of published materials (e.g., journal articles); it includes authorship, citation, and journal analysis, as well as analysis of publication impact. Bibliometric analysis can be used to track trends in publication regarding renewable energy, including tendency to discuss specific topics and technologies, as well as any changes in the primary geographic sources of publication. Dong et al (2012) performed a bibliometric analysis on solar power-related research published between 1991-2010 (Dong, Xu, Luo, Cai, & Gao, 2012). The results showed that the US has the greatest counts of single-country PV-related publications, and it was also a frequent partner in international collaboration, contributing to 13.5% of all the international collaborative articles during the analysis period. However, the influence of other countries is increasing, with Mainland China and South Korea, in particular, showing the highest growth pace since 2006 (Figure KC.9). Dong et al (2012) indicated that publications on solar power research have predominantly focused on fundamentals such as chemistry and materials. Regarding article keywords, “solar cell” was by far the dominant topic, in line with its importance in global patenting. Some solar energy-related publications include discussion of inverters and other BOS components, but stages of the PV module value chain are the most common topics. We note that a small body of literature addresses inverters for residential PV (e.g., Guo et al. 2014, Shayestegan, et al. 2018, etc.) and that more publications specific to inverters for utility-scale solar may become available in the future.
Figure KC.9 Numbers and trend of solar power technology related publications by country (left chart) and by keyword (right chart), 1991-2010
Source: Figure 3 and Figure 4 from (Dong, Xu, Luo, Cai, & Gao, 2012)
Knowledge Spillover
Knowledge spillover is hard to measure because it tends to be less tangible or readily observable than other innovation metrics. Spillover can be considered between companies, between public and private institutions, and across geographies. Firms often aim to appropriate the returns of innovation (i.e., prevent knowledge spillover) through patents or secrecy. Backward and forward citation of patents can reveal connections between the knowledge imbedded in technologies; backward citation refers to patents cited in the patent of interest, while forward citation refers to patents citing the patent of interest. Patent citation and patent family size are commonly used to measure patent quality. Patent family size refers to the number of patents taken multiple countries to protect the same invention. Kim et al (Kim, Kim, & Kim, 2018) confirmed that a renewable energy firm’s patent protection ability is important for its financial performance. They also found that the backward citation of patents and the size of patent families, more so than simple patent counts, are important measures of the ability of firms in this industry to protect the knowledge they develop. As Figure KC.10 shows, US PV patents have retained the highest quality (i.e., number of forward citations), particularly for core and downstream technologies, even though the US share of total PV patents has been declining since 2005; “downstream technologies” include inverters and other BOS components. Figure KC.11 shows patent family size, including for downstream technologies.
Figure KC.10 Average Forward (citing) Citations of Patents Filed in Major Solar PV (1st Generation) Countries
Source: Panel (A) of Figure B.1 from (Binz et al., 2017)
Figure KC.11 Average Patent Family Size of Patents Filed in Major Solar PV (1st Generation) Countries
Source: Panel (B) of Figure B.1 from (Binz et al., 2017)
Research Collaboration
Knowledge can also spread as teams collaborate on research and publication. Network analysis, the characterization and investigation of social structures through graphs of nodes (e.g., people, things) and linkages (e.g., relationships, interactions), can be used to explore the connections between different groups of researchers involved in the same field of study. Regarding research collaboration, (de Paulo & Porto, 2017) conducted a bibliometric and social network analysis for publications on solar technologies (including PV, CSP, and solar thermal). They identified five cooperation networks within the field (see Figure KC.12). The analysis showed that the US (cluster B, and to a lesser extent, cluster E) is notable for its numerous national and international partnerships; US organizations (cluster B) serve as a hub for the whole network, mediating relationships between different clusters. These organizations include NASA, Washington University, the National Institute for Environmental Studies, CALTECH and many others. Among other network participants, China (cluster A) engages in a high degree of local and national cooperation; European countries (cluster C and D) are notable for international partnerships. Note that while this bibliometric analysis reviewed all forms of solar energy technology, its general findings regarding research centers and collaborative linkages likely hold true for utility PV BOS technologies.
Figure KC.12 Solar Co-Authorship Network of Organizations
Source: Figure 9 from (de Paulo & Porto, 2017)
Notes: A: Predominantly Chinese organizations and others from Asian and US; B: Mostly from USA, a hub for the whole network; C: European organizations from Denmark, Germany, Belgium, France and the Netherlands; D: European organization and others including Spain, Finland, England, Switzerland, India and Turkey; E: mostly US domestic organizations.
Tacit Knowledge Transfer
Tacit knowledge is a primary source of research spillovers because this type of knowledge is embodied in the skills of the firm’s employees. Tacit knowledge includes rules of thumb, heuristics, and tricks of the trade, as compared to the knowledge embodied in a technology; it may be related to products or processes. Informal exchanges between researchers and job turnover between firms are channels through which tacit knowledge flows from one firm to the other (Kaiser, 2002). Indicators such as number of R&D personnel and innovation expenditure could be considered a proxy for tacit knowledge transfer, although the validity of this proxy will depend on the type of tacit knowledge to be transferred. Generally, intrafirm transfer modes involving a high level of direct human interaction (e.g., joint ventures, international research centers) are more effective than so-called arms-length transfer mechanisms (e.g., personnel relocating) In the case of PV technologies, tacit knowledge could be exchanged through human resource transfer, training, joint R&D programs, process licensing, trade in production equipment, and trade in turnkey production facilities (Quitzow, Huenteler, & Asmussen, 2017). Figure KC.13 provides an example of international tacit knowledge transfer for solar PV technology.
Figure KC.13 An Example of International Tacit Knowledge Transfer for Dolar PV Technology
Source: Figure 1 from