How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

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

The Failure of the Strong Pumping Lemma for Multiple Context-Free Languages

International audienceSeki et al. (Theoretical Computer Science 88(2):191–229, 1991) showed that every m-multiple context-free language L is weakly 2m-iterative in the sense that either L is finite or L contains a subset of the form $\{u_0 w_1^i u_1 \dots w_{2m}^i u_{2m} \mid i \in \mathbb{N}\}$, where $w_1 \dots w_{2m} \neq \epsilon$. Whether for every m-multiple context-free language L is 2m-iterative, that is to say, whether all but finitely many elements $z$ of L can be written as $z=u_0 w_1 u_1 \dots w_{2m} u_{2m}$ with $w_1 \dots w_{2m} \neq \epsilon$ and $\{u_0 w_1^i u_1 \dots w_{2m}^i u_{2m} \mid i \in \mathbb{N}\} \subseteq L$ has been open. We show that there is a 3-multiple context-free language that is not k-iterative for any k

Tackling Cost Drivers of Maritime UXO Surveys

During UXO survey and clearance campaigns, a large number of false positive target points drive costs and efforts. This paper presents three approaches for the improvement of maritime UXO surveys that allow for the immediate execution of target point investigations. (1) Data quality factors were generated to help determine, whether UXO can be detected in the acquired multibeam echosounder (MBES), side-scan sonar, subbottom profiler and magnetic data. (2) Artificial intelligence was developed to rapidly identify target points with potential UXO objects in MBES data. (3) Magnetometers and a high-resolution camera were integrated on an AUV to allow for autonomous target point investigation, that does not require permanent supervision of survey personnel