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首页> 外文会议>Annual Conference and Expo of the Institute of Industrial Engineers >Two-Stage and Multi-Stage Stochastic Unit Commitment under Wind Generation Uncertainty
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Two-Stage and Multi-Stage Stochastic Unit Commitment under Wind Generation Uncertainty

机译：风力发电不确定性下的两阶段和多阶段随机机组组合

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摘要

Although wind power is considered green energy and free, its intermittent nature causes the instability issues for the current power grid system. The two-stage stochastic optimization approach has been explored recently and justified as an effective approach to achieve cost efficiency while ensuring system reliability. This approach can fit well for most independent system operators (ISOs) today, performing reliability unit commitment (RUC) runs to ensure the sufficient thermal generation capacity to accommodate the wind output fluctuation. In this paper, we propose a multistage stochastic optimization unit commitment model and compare its performance with the two-stage stochastic optimization model, in terms of total expected cost and computational complexity. For the multi-stage stochastic optimization model, the unit commitment and economic dispatch decisions are made sequentially as more information is realized, which can take advantage of more realized information and provide more flexibility for unit commitment decisions to accommodate the uncertainty. The stochasticity of wind power output is represented by a scenario tree and the final computational results verify the value of the multi-stage stochastic optimization approach.
机译：尽管风能被认为是绿色能源并且是免费的，但其间歇性却导致了当前电网系统的不稳定问题。最近已经研究了两阶段随机优化方法，并将其证明为在确保系统可靠性的同时实现成本效益的有效方法。这种方法非常适合当今大多数独立系统运营商（ISO），通过执​​行可靠性单位承诺（RUC）来确保足够的热量产生能力以适应风输出波动。在本文中，我们提出了一个多阶段随机优化单元投入模型，并将其性能与两阶段随机优化模型进行比较，以总预期成本和计算复杂度为单位。对于多阶段随机优化模型，随着更多信息的实现，单位承诺和经济调度决策将按顺序进行，这可以利用更多已实现的信息，并为单位承诺决策提供更大的灵活性以适应不确定性。风电输出的随机性由情景树表示，最终的计算结果验证了多阶段随机优化方法的价值。

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