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Sacha Hodencq

Publications and source records attributed to Sacha Hodencq.

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Standardizing case study descriptions for multi-energy systems and networks modeling

Research on Multi-Energy Systems (MES) often relies on case studies with divergent hypotheses and terminologies, limiting comparability and slowing progress. Discussions at the ECOS 2025 conference highlighted the need for standardized reference case studies to facilitate reuse and comparison. While frameworks like the IEC 62559 standard and the Open Energy Platform (OEP) exist, their adoption for MES remains fragmented. This heterogeneity hinders collaboration and replicability, motivating efforts towards a unified description framework tailored to MES. This paper aims to address this gap by evaluating existing approaches in order to promote a standardized description framework for MES case studies. The goal is to enhance comparability, streamline research, and make a first step towards defining reference case studies and benchmarks in the domain. The study adopts a collaborative approach: after analysing existing description frameworks and selecting the most suitable one, the co-authors describe their own case studies, followed by cross-reviews to assess completeness, clarity, and openness of data/models. The description framework is adapted to emphasizeMES-specific elements, such as system configuration and use case details. A checklist is developed to guide reviews. Preliminary results include a set of standardized case study descriptions and insights from cross-reviews on framework strengths/limitations. The diversity of case studies underscores the framework's flexibility, while feedback reveals opportunities for improvement and broader adoption. This work provides a foundation for standardized MES case study descriptions, fostering collaboration, comparability, and replicability. By reducing ambiguity and ensuring the availability of relevant information in a consistent format, it accelerates research and benchmarking in the field.

eess.SY

Opportunities for Hybrid Modeling Approaches in Energy Systems optimization

This paper surveys the primary computational hurdles of Energy Systems optimization coming from different sources: model-induced complexity, optimization algorithm requirements, and uncertainties handling (both aleatoric and epistemic). Techniques to reduce complexity such as time-series and spatial aggregation, model order reduction, and specialized optimization strategies are reviewed for their effectiveness in balancing computational feasibility and model fidelity. Furthermore, Various uncertainty-management frameworks, including scenario-based approaches, robust optimization, and distributionally robust methods, are reviewed and their limitations in scaling and data requirements are discussed. The potential of hybrid modeling emerges as a key avenue: by fusing mechanistic and machine learning elements, hybrid techniques for modelling and optimization can harness the strengths of both worlds while mitigating their respective drawbacks. The paper highlights several directions for further research to develop advanced methods to tackle the complexity of MES.

math.OC