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In course of the energy transition, the growing share of Renewable Energy Sources (RES) makes electricity generation more decentralized and intermittent. This increases the relevance of exploiting flexibility potentials that help balancing intermittent RES supply and demand and, thus, contribute to overall system resilience. Digital technologies, in the form of automated trading algorithms, may considerably contribute to flexibility exploitation, as they enable faster and more accurate market interactions. In this paper, we develop an integrated algorithmic framework that finds an optimal trading strategy for flexibility on multiple markets. Hence, our work supports the trading of flexibility in a multi-market environment that results in enhanced market integration and harmonization of economically traded and physically delivered electricity, which finally promotes resilience in highly complex electricity systems.

There are different flexibility options to align power systems to volatile feed-in of renewable electricity sources. The flexibility options differ in the dimensions of time, spatiality, and resource type. To make policy decisions on future energy systems, it is necessary to get a top-down indication of how much power system flexibility is needed. With the ongoing energy transition, there is yet no comprehensive overview of indicators that describe which dimension of flexibility will be necessary to what extent for different energy systems. Therefore, this paper provides a first overview of indicators that can be used to assess the necessity of power system flexibility. Thus, we do a systematic literature review to identify indicators that allow us to estimate the necessity of power system flexibility. We conduct a meta-analysis of these indicators and categorize them as indicators that either stand for an increasing or decreasing necessity of power system flexibility. Our paper can help inform policy, assess needed changes to system operations, increase stakeholder acceptance and investor confidence in implementing new technology and measures.

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