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Cooperative algorithmic-level decompositions are a parallel computation method that executes several local algorithms. These local algorithms in turn exchange information to find better or more robust solutions. In energy management, this paradigm is used for multi-agent systems to manage generation and/or consumption of electric power within coalitions of energy resources. The exchanged data contains operational schedules of other group members. Such data can be aggregated and exploited by agents to find better solutions and derive sensitive information. It is thus desirable to keep the data private. We apply the Paillier cryptosystem to the use case of distributed predictive scheduling in virtual power plants or energy districts. We had to extend the interaction protocol as simply securing the communication channel between the agents is not targeted for this type of algorithm. Calculations of the global objective function are now done as a joint distributed task with homomorphic encryption. We show that the privacy version has a competitive performance in terms of solution quality with only a constant increase in message exchange for larger scenarios.

Energy research software (ERS) is a central cornerstone to facilitate energy research. However, ERS is developed by researchers who, in many cases, lack formal training in software engineering. This reduces the quality of ERS, leading to limited reproducibility and reusability. To address these issues, we developed ten central recommendations for the development of ERS, covering areas such as conceptualization, development, testing, and publication of ERS. The recommendations are based on the outcomes of two workshops with a diverse group of energy researchers and aim to improve the awareness of research software engineering in the energy domain. The recommendations should enhance the quality of ERS and, therefore, the reproducibility of energy research.

The IEC 62559 Use Case Methodology and Smart Grid Architecture Model (SGAM) Framework are crucial for fostering a shared understanding in the development of ICT-based power systems and their components within energy-related Requirements Engineering. However, discrepancies in interpretation among diverse stakeholders often diminish the quality of IEC 62559 Use Cases and SGAM Models, potentially leading to errors and costly setbacks in later project stages. This paper introduces the Smart Grid Assistive AI in Requirements Engineering (SGAAIRE), an intelligent system that leverages Large Language Models to enhance the quality of IEC 62559 Use Case descriptions and SGAM Models. Through a demonstration scenario featuring built-in quality defects, SGAAIRE is shown to effectively address common issues as an extension to a Use Case Management Repository. The proposed system supports the improvement of IEC 62559 Use Cases and SGAM Models, enabling the development of third-party tools aimed at advancing research in AI for energy-related requirements management.

This short paper aims to shed light on the impact of malicious agents in multi-agent energy systems controlled in a decentralized fashion, and present an effective method to address this vulnerability.

Reinforcement learning (RL) algorithms are increasingly used to solve the optimal power flow (OPF) problem. Yet, the question of how to design RL environments to maximize training performance remains unanswered, both for the OPF and the general case. We propose a general approach for automated RL environment design by utilizing multi-objective optimization. For that, we use the hyperparameter optimization (HPO) framework, which allows the reuse of existing HPO algorithms and methods. On five OPF benchmark problems, we demonstrate that our automated design approach consistently outperforms a manually created baseline environment design. Further, we use statistical analyses to determine which environment design decisions are especially important for performance, resulting in multiple novel insights on how RL-OPF environments should be designed. Finally, we discuss the risk of overfitting the environment to the utilized RL algorithm. To the best of our knowledge, this is the first general approach for automated RL environment design.