| 30 Oct 2025
A Decision Support System for the Continuous Economic Evaluation of Wind Farms
Abstract. This paper presents a decision support system that integrates digital twin technology with advanced economic evaluation tools to enable continuous and holistic assessment of wind farm investments. The framework combines real-time turbine health data, including remaining useful life estimates derived from SCADA and condition monitoring systems, with financial models employing discounted cash flow, scenario testing, Monte Carlo simulations, and real-options valuation. Implemented through the open-source DigiWind platform, the system adheres to FAIR data principles and provides a flexible, interoperable environment for asset management. A case study on an 8 MW wind turbine in Germany demonstrates the framework’s ability to guide decisions such as life extension, repowering, decommissioning, or sale under volatile market conditions. Results highlight the importance of coupling technical reliability forecasts with market-based financial outlooks to capture both risks and upside potential, offering a scalable and transparent tool for investors, operators, and policymakers navigating the evolving wind energy sector.
General Comment:
The manuscript addresses an relevant and timely topic: integrating digital-twin-based asset health information with long-term economic evaluation for wind farms. The conceptual framework presented is sound, and the overall vision of linking SCADA/CMS-based RUL estimation with investment decision support is compelling.
My main concern lies in the simplicity and limited depth of the case study. Although the earlier sections of the paper emphasize detailed RUL modelling, physics-informed degradation assessment, and component-level trajectories, the case study ultimately incorporates RUL only as fixed replacement times. As a result, the economic model is driven by binary electricity-price scenarios rather than integration of dynamic or uncertain RUL estimates. A more comprehensive case study, that incorporates RUL uncertainty, component-specific degradation paths and connected interventions would strengthen manuscript.I therefore recommend major revision.
Individual Comments:
Abstract:
The abstract could be improved by providing a more detailed picture of the study.
- how are health metrics derived
- main components/functionality of the financial model
- which kind of scenarios are tested through monte carlo analyisis
- A brief summary of key results
Introduction:
Line 30: I would challenge the statement that methods have not progressed in the last 20 years. Please specify.
Line 37: Please specify the "varying needs" of each turbines
Line 43 and following : It would be useful to give an overview of key methodologies for RUL estimation. What are main assumption and limitations? Also it would be worth mentioning that RUL estimation from SCADA data is connected to high uncertainty as lifetime may not be determined by fatigue.
Section 2.1:
The description of the DigiWind digital twin framework is somewhat generic and does not convey clearly how the architecture supports the specific integration tasks discussed later. The text lists several software concepts (microservices, MQTT, Docker, BPMN) but does not explain their functional relevance in the context of the decision-support workflow. I suggest revising the section and potentially integrating an overview figure.
Section 2.2:
feels too high-level and does not provide enough methodological detail to understand how RUL is actually computed. Which components and failure modes are targeted by the surrogate model?
How are load–damage relationships derived and how is partial-operation scaling is applied. As the economic evaluation highly depends on the RUL information the manuscript would benefit from more detail.
Section 2.2 (section numbering is duplicated). The authors list several methods (DCF, Monte Carlo simulation, scenario testing, real-options valuation), but the manuscript does not describe how these techniques are implemented. For example, which variables are sampled in the Monte Carlo simulation, how uncertainties are parameterized,or how RUL estimates enter the cash-flow model. Likewise, the Real-Options methodology is mentioned but not explained.
Additionally, the reference to future integration of Markov Decision Processes should be moved to future work section.
Section 2.3:
The authors introduce the WESgraph ontology, a SPARQL query, and a BPMN workflow diagram, but I find it difficult to understand how these concepts interact operationally. Are SPARQL queries are executed at runtime to determine connections? Is the ontology used at runtime? The section would benefit from a clearer explanation of the operational workflow.
Also please explain how FAIR data principles are ensured.
Section 3: The case study tests different stochastic scenarios describing the transition from feed-in tariff market price while the market price is assumed to be a constant value. All other parameters are deterministic and fixed, particularly also the RUL estimates. So, in my understanding, the case study is merely a financial sensitivity test on future electricity pricing, not really an integrated techno-economic RUL decision problem. Uncertainty in RUL estimation, the degradation trajectory, or some updating through the digital twin is not considered.
Results: Please provide a more detailed explanation of how the real option value is calculated. Some interpretation on the double-peak shape of the NPV distribution would be helpful as well. Legends for for NPV/ROV level lines would be helpful in the plots.