A chance-constrained optimization framework for wind farms to manage fleet-level availability in condition based maintenance and operations
A chance-constrained optimization framework for wind farms to manage fleet-level availability in condition based maintenance and operations
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DOI：
10.1016/j.rser.2022.112789
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发表时间：
2022-10
影响因子：
15.9
通讯作者：
F. Fallahi;I. Bakir;M. Yildirim;Z. Ye
中科院分区：
文献类型：
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作者：
F. Fallahi;I. Bakir;M. Yildirim;Z. Ye
运营和维护(O&M)是风电场支出的关键因素。为了提高竞争力，风电场运营商越来越多地考虑利用来自状态监测(CM)系统的实时传感器数据。CM对风力涡轮机不断演变的资产故障风险提供了重要的见解。到目前为止，由于与决策的特别联系，这些见解还没有在风电场运营和维护中得到充分利用。具体地说，风力发电场中的CM应用仅限于检测具有即将发生故障的风险、需要立即更换的涡轮机。实际上，风电场维护需要仔细主动地协调各涡轮机之间的运维依赖关系，以及与资产可用性、运营和市场状况相关的多个不确定性来源。本文提出了一种统一的基于状态的风电场维护和运行调度方法，该方法模拟了与风电机组可用性、风电发电量和市场价格相关的不确定性。提出的公式明确考虑了运行和维护中的涡轮机对涡轮机的依赖关系，例如机会性维护，以确定对多个风电场最优的运营和维护决策。该问题被描述为机会约束的随机规划模型，以在确保高水平的涡轮机可用性和发电量的同时最大化运营收入。为了使机会约束易于处理，提出了两种近似，重点是样本平均近似(SAA)和显著的尾部不等式，如马尔可夫不等式和Chernoff界。我们在一组综合实验上的结果表明，所提出的方法在大规模风电场的资产可用性、市场收入和维护成本方面都有显著的改善。
Operations and maintenance (O&M) is a key contributor to wind farm expenditures. To increase competitiveness, wind farm operators are increasingly looking into leveraging real-time sensor data from condition monitoring (CM) systems. CM provides significant insights on evolving asset failure risks for wind turbines. To date, these insights have not been fully leveraged in wind farm O&M due to ad-hoc connections to decision-making. Specifically, CM applications in wind farms have been limited to detection of turbines with imminent failure risks that require immediate replacement. In reality, wind farm maintenance requires a careful proactive orchestration of O&M dependencies across turbines along with multiple sources of uncertainty associated with asset availability, operational and market conditions. This paper proposes a unified condition-based maintenance and operations scheduling approach for wind farms that models uncertainties related to turbine availability, wind power output and market price. The proposed formulation explicitly considers the turbine-to-turbine dependencies in operations and maintenance, such as opportunistic maintenance, to identify the O&M decisions that are optimal for multiple wind farms. The problem is formulated as a chance-constrained stochastic programming model to maximize operational revenue while ensuring high levels of turbine availability and generation. To make the chance constraints tractable, two approximations are proposed with a focus on sample average approximation (SAA) and prominent tail inequalities such as Markov’s inequality and Chernoff bound. Our results on a comprehensive set of experiments demonstrate that the proposed approach provides significant improvements in asset availability, market revenue and maintenance costs in large scale wind farms.