发表机构
Department of Industrial and Systems Engineering, Rutgers University（罗格斯大学工业与系统工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对电网与电力市场背景下风电场维护规划研究不足的问题，提出战略性维护规划模型，结合IEEE RTS 96节点测试平台实验，发现风电场可在低系统成本影响下提升利润。
AI 中文摘要
风力涡轮机需要定期维护，由此产生的成本是风电场电力生产成本的重要组成部分。因此，人们一直致力于优化风力涡轮机的维护调度，以在维护成本与故障或计划外维修风险之间找到最优平衡。而在电网条件和电力市场出清的背景下进行风电场维护调度的机会，仍是研究不足的领域。本文通过在电网和电力市场背景下对风电场维护规划进行建模与研究，致力于缩小这一差距。同时，本文还回顾了美国当前风电场维护调度的实践，这为本文及其模型提供了动机。本文的重点是推导战略性维护规划问题及高效的求解方法，其中风电场运营商旨在提交降额后的风电场容量，使得由此产生的市场出清和电价能最大化其利润，同时确保所有必要的维护都能完成。本文推导并研究了该模型的确定性版本和随机版本，后者考虑了环境和运营不确定性。我们使用IEEE RTS 96节点测试平台结合真实世界的海上风电场数据进行数值实验，研究了风电场及涡轮机规模、预测质量的作用。我们观察到战略性与电网服务型维护规划之间存在一致性，并发现风电场可在对系统成本影响较小的情况下提升自身利润。
英文摘要
Wind turbines require regular maintenance and the resulting costs are a substantial component of a wind farm's cost of electricity production. As a result, there has been ongoing interest in improving wind turbine maintenance scheduling to find an optimal balance between maintenance costs and the risk of failure or unplanned repairs. What remains understudied is the opportunity for wind farms to schedule maintenance in the context of grid conditions and electricity market clearing. This paper contributes to closing this gap by modeling and studying wind farm maintenance planning in a grid and electricity market context. We also review current U.S. practice of wind farm maintenance scheduling, which motivates this paper and its models. Our focus is the derivation of a strategic maintenance planning problem, alongside an efficient solution approach, in which the wind farm operator aims to submit a derated wind farm capacity such that the resulting market clearing and electricity prices maximize its profit while ensuring that all required maintenance can be performed. We derive and study both deterministic and stochastic versions of the model, with the latter considering environmental and operational uncertainties. We conduct numerical experiments using the IEEE RTS 96-bus testbed with real-world offshore wind farm data and investigate the roles of farm and turbine size, as well as forecast quality. We observe an alignment between strategic and grid-serving maintenance planning and find that wind farms can improve their bottom line with low impact on system costs.
