With a higher share of renewables in the energy system and due to their intermittent character, enabling technologies get more and more important that help to balance the grid or make surplus energy usable. Within our project “Network Analysis and Simulation of Innovation Dynamics for new Key Technologies in the Energy Sector (InnoSEn)” we take a closer look at the research and innovation networks of key emerging technologies for the storage of power from renewable resources in Germany.
We follow the approach of analyzing knowledge dynamics in innovation networks (cf Ahrweiler 2010). Thus, we argue that technological innovations initially are created by and diffuse via research and knowledge flows within and around firms and their networks. Within the course of the project, we will model these actors in knowledge-market-resources environment.
To frame technological innovation system elements and functions, approaches from different fields have been made, namely the System of Innovation, Technological Innovation System Analysis (TIS), Multi-Level Perspective, Transition Management and Strategic Niche Management. By analysing system elements, these approaches help to identify systemic problems as well, as system problems are defined as hindrances to the functioning of the system (Negro et al 2012, Wieczorek and Hekkert 2012). The basic processes of knowledge exchange between firms, research institutions and further actors in the field are analysed in more detail by additionally differentiating various relevant dimensions of knowledge and their relevance for innovation processes (Droste-Franke et al 2015, Kline and Rosenberg 1986). An important aspect in this context is that multiple sectors are involved for the implementation of new technologies into the energy system, each with its own knowledge and experience and differences in the application and complementation of knowledge. Stephan et al (2017) show that exchange of knowledge can differ considerably between the sectors at implementing energy storage technologies.
In our paper, we present first results in form of analyses and visualisations of research networks on storage technologies, especially lithium batteries in Germany. In addition, we operationalise TIS elements and functions to identify barriers for further industry development, particularly with respect to knowledge flows, which builds the basis of our empirical work. Designed to feed the empirically grounded, agent-based model “SKIN Energy”, our data will primarily consist of statistical, interview an expert group data.
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Droste-Franke B et al (2015). Improving Energy Decisions. Towards Better Scientific Policy Advic e for a Safe and Secure Future Energy System, Springer
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Negro SO et al (2012) Why does renewable energy diffuse so slowly? A review of innovation system problems. Renewable and Sustainable Energy Reviews 16, 3836– 3846
Stephan et al (2017) The sectoral configuration of technological innovation systems. Research Policy 46, 709-723
Wieczorek AJ and Hekkert MP (2012) Systemic instruments for systemic innovation problems: A framework for policy makers and innovation scholars. Science and Public Policy 39, 74–87