How can energy-system models inform technology development? Insights for emerging energy-storage technologies

Energy-system models (ESMs) are used often to support policymakers, system operators, or investors, but much less for offering guidance to technology developers. This paper contributes to bridging the gap between the ESM discipline and technology developers. For the example of emerging energy-storage technologies, we identify and categorise information needs of technology developers. Moreover, we discuss, which information needs can be met by an advanced analysis of ESM results, and which needs require fundamental model developments. We demonstrate the capabilities of an advanced analysis for an application study using a model that optimises investment and dispatch to assess energy-storage technology requirements in a fully decarbonised European power system. Our analysis provides insights regarding requirements and opportunities of energy-storage technologies in terms of design parameters, operational patterns, and target markets. We show that technologies with low-cost power, e.g. , lithium-ion batteries (LIB), are designed with low energy-to-power (E2P) ratios, while those with low-cost energy ( e.g. , H 2 ) have high E2P ratios. Concerning operational patterns, low-E2P energy-storage technologies cycle frequently (up to daily for LIB and vanadium-redox flow batteries), whereas H 2 featuring a high-E2P cycles only a few times per year. Moreover, we find that requirements and cycling frequencies vary strongly between different target markets driven by their underlying electricity systems. We conclude that an advanced analysis can make a contribution to bridging the gap between ESMs and technology developers. Future work should improve the representation of technological details and develop inverse modelling approaches for technologies in very early development stages with still highly uncertain parameters. • Energy-system models inform developers of energy-storage technologies. • European electricity-system model for 2050 with four promising energy-storage technologies. • Study energy-to-power ratios, full-cycle equivalents, dispatch curves, and target markets. • Developers supported with operational requirements and trade-offs in design decisions. • Inverse-modelling approach needed for technologies in very early development stages.