Large Language Models (LLMs) have the potential to support the transformation of natural language legal text into a regulatory model, a task conventionally known to be time consuming and error prone when done manually. In this paper, we introduce CLERK: a C ompanion L LM E xpert for modeling R egulatory K nowledge existing in natural language legal texts. CLERK captures regulatory knowledge in the format of Legal Goal Requirements Language (GRL) models. CLERK offers three key contributions, utilizing established prompting techniques: (1) Adopting the Tree-of-Thought (ToT) prompting framework, CLERK streamlines the regulatory modeling process by breaking down complex steps into manageable tasks and focusing on those essential for constructing a Legal GRL model only. (2) The ToT framework enables self-evaluation of intermediate outputs. (3) CLERK enhances consistency and clarity, by leveraging additional in-context learning prompting techniques, such as few-shot prompting and output formatting with an explicit syntax definition. Experiments with eight regulatory articles from two domains (healthcare and energy communities) display a notable improvement brought about by CLERK compared to previous approaches. This improvement pertains to identifying relevant actors, goals and their deontic modalities, as well as the relationships among goals.

To support the value assessment of technically feasible smart grid initiatives there exist several valuation methods. To determine whether those methods address all concerns relevant for smart grid valuation, we carry out a literature analysis aiming at (1) identifying existing valuation methods and the steps they propose, (2) identifying important valuation considerations, and (3) confronting these considerations with artifacts proposed by the existing valuation methods to identify open issues, requirements, and remaining challenges. Based on the conducted analysis we identify, among others, the following main deficiencies: (1) only a limited scope of concerns relevant to valuation is covered, particularly a systematic consideration of stakeholders goals, value exchange scenarios, and the IT infrastructure is lacking; and (2) a lack of instruments dedicated to fostering accessibility of valuation, in terms of establishing a shared understanding, communicating results, or actively involving different stakeholders in the process. Based on the findings, we suggest the application of conceptual modeling as an instrument to address the identified deficiencies. Therefore, we reflect on the role that current modeling approaches can play in smart grid valuation. This paper is a part of a larger project whose ultimate goal is to develop a model-based method for multi-perspective valuation of smart grid initiatives. The purpose of this paper is to establish a foundation for the realization of the envisioned method. The design of the model-based valuation method itself, its application and evaluation, are subjects of future work.