An expert survey to assess the current status and future challenges of energy system analysis

Decision support systems like computer-aided energy system analysis (ESA) are considered one of the main pillars for developing sustainable and reliable energy transformation strategies. Although today’s diverse tools can already support decision-makers in a variety of research questions, further developments are still necessary. Intending to identify opportunities and challenges in the field, we classify modelling capabilities (32), methodologies (15) implementation issues (15) and management issues (7) from an extensive literature review. Based on a quantitative expert survey of energy system modellers (N=61) mainly working with simulation and optimisation models, the status of development and the complexity of realisation of those modelling topics are assessed. While the rated items are considered to be more complex than actually represented, no significant outliers are determinable, showing that there is no consensus about particular aspects of ESA that are lacking development. Nevertheless, a classification of the items in terms of a specially defined modelling strategy matrix identifies capabilities like land-use planning patterns, equity and distributional effects and endogenous technological learning as “low hanging fruits” for enhancement, as well as a large number of complex topics that are already well implemented. The remaining “tough nuts” regarding modelling capabilities include non-energy sector and social behaviour interaction effects. In general, the optimisation and simulation models differ in their respective strengths, justifying the existence of both. While methods were generally rated as quite well developed, combinatorial optimisation approaches, as well as machine learning, are identified as important research methods to be developed further for ESA.

💡 Research Summary

This paper investigates the present state and future challenges of computer‑aided Energy System Analysis (ESA), a cornerstone for designing sustainable and reliable energy transition pathways. The authors begin with an extensive literature review that yields a taxonomy of 32 modelling capabilities, 15 methodological approaches, 15 implementation issues, and 7 management concerns. These categories capture both traditional functions—such as power‑flow calculation, demand‑supply balancing, and renewable capacity optimisation—and emerging topics like land‑use planning, equity and distributional effects, endogenous technological learning, cross‑sector interactions, and behavioural modelling.

To assess how these items are perceived by practitioners, a quantitative survey was conducted with 61 energy‑system modellers, the majority of whom work with simulation or optimisation tools. Respondents rated each item on two separate 5‑point Likert scales: (1) the degree to which the capability or method is currently developed, and (2) the perceived complexity of implementing it in practice. Statistical analysis shows a consistent tendency to view most items as more complex than they are actually realised, indicating a gap between theoretical availability and practical deployment. No single item emerged as a clear outlier, suggesting that the community lacks a consensus on which aspects of ESA are most under‑developed.

The authors then map the survey results onto a purpose‑built “modelling strategy matrix”. The matrix’s axes are development level (low–high) and implementation complexity (low–high), producing four quadrants. Items placed in the lower‑right quadrant—labelled “low‑hanging fruit”—include land‑use planning patterns, equity/distributional effects, and endogenous learning. These are judged to be relatively easy to implement yet highly valuable, making them prime targets for short‑term research investment. The upper‑right quadrant contains “well‑implemented” features that are both mature and easy to use, such as basic power‑flow and optimisation algorithms. The upper‑left quadrant houses “advanced but complex” topics, notably non‑energy sector interactions and social‑behaviour modelling, which are recognised as essential for nuanced policy analysis but remain technically demanding. Finally, the lower‑left quadrant comprises “tough nuts” – capabilities that are both under‑developed and perceived as highly complex, requiring substantial methodological breakthroughs and data integration efforts.

Methodologically, respondents generally consider existing optimisation and simulation techniques to be sufficiently mature. However, combinatorial optimisation and machine‑learning approaches received lower development scores, highlighting a need for further research in high‑dimensional search, surrogate modelling, and data‑driven decision support. The paper underscores that optimisation models excel at delivering cost‑optimal solutions, whereas simulation models are better suited for scenario exploration and uncertainty analysis; both are therefore indispensable and complementary.

Limitations are acknowledged: the sample is skewed toward modelers familiar with optimisation/simulation, potentially under‑representing the views of policymakers, industry practitioners, or experts in newer digital technologies such as digital twins or blockchain‑based energy markets. Moreover, the survey items, while comprehensive, may not capture the very latest methodological innovations.

In conclusion, the study provides a structured, data‑driven roadmap for ESA development. By distinguishing “low‑hanging fruit” from “tough nuts”, it guides researchers and funding agencies toward areas where modest effort can yield high policy relevance, as well as those that demand long‑term, interdisciplinary investment. The findings also reinforce the rationale for maintaining both optimisation and simulation toolchains, and they call for intensified work on combinatorial optimisation and machine‑learning techniques to keep ESA aligned with the growing complexity of future energy systems.