Department of Management Science, University of Strathclyde, Glasgow, Scotland.
Risk Anal. 2022 Jul;42(7):1524-1540. doi: 10.1111/risa.13846. Epub 2021 Nov 27.
Financial stakeholders in offshore wind farm projects require predictions of energy production capacity to better manage the risk associated with investment decisions prior to construction. Predictions for early operating life are particularly important due to the dual effects of cash flow discounting and the anticipated performance growth due to experiential learning. We develop a general marked point process model for the times to failure and restoration events of farm subassemblies to capture key uncertainties affecting performance. Sources of epistemic uncertainty are identified in design and manufacturing effectiveness. The model then captures the temporal effects of epistemic and aleatory uncertainties across subassemblies to predict the farm availability-informed relative capacity (maximum generating capacity given the technical state of the equipment). This performance measure enables technical performance uncertainties to be linked to the cost of energy generation. The general modeling approach is contextualized and illustrated for a prospective offshore wind farm. The production capacity uncertainties can be decomposed to assess the contribution of epistemic uncertainty allowing the value of gathering information to reduce risk to be examined.
海上风电场项目的财务利益相关者需要对能源产能进行预测，以便在建设之前更好地管理与投资决策相关的风险。由于现金流贴现和经验学习导致的预期性能增长的双重影响，早期运行寿命的预测尤其重要。我们开发了一个通用的标记点过程模型，用于农场组件的失效和恢复事件，以捕获影响性能的关键不确定性。在设计和制造效果中确定了认知不确定性的来源。然后，该模型捕获了跨组件的认知和随机不确定性的时间效应，以预测农场可用性信息的相对容量（给定设备技术状态的最大发电容量）。该性能指标可将技术性能不确定性与发电成本联系起来。一般的建模方法针对一个预期的海上风电场进行了说明和说明。可以对生产能力不确定性进行分解，以评估认知不确定性的贡献，从而可以检查收集信息以降低风险的价值。
