Risk-constrained energy management with multiple wind farms
Risk-constrained energy management with multiple wind farms
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DOI：
10.1109/isgt.2013.6497884
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
Yu Zhang;Nikolaos Gatsis;G. Giannakis
中科院分区：
文献类型：
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作者：
Yu Zhang;Nikolaos Gatsis;G. Giannakis
为了在未来的智能电网中实现高风电普及率的目标，考虑到风电的随机性质的经济能源管理是至关重要的。研究了具有多个风电场的电力系统多周期经济调度和需求侧管理问题。针对不可调度风电资源的随机性问题，提出了基于负荷损失概率(LOLP)的机会约束优化问题，以限制供需失衡风险。针对风力发电时空联合分布的复杂性，提出了一种新的基于蒙特卡罗抽样的情景逼近方法。诱人的是，问题结构被利用来获得无样本大小的问题公式，从而使得即使在很长的调度时间范围内也能够适应非常小的LOLP要求。最后，为了捕捉多个风电场功率输出之间的时间和空间相关性，在风速分布模型和风速-功率-输出映射的基础上引入自回归模型来生成所需的样本。数值结果证实了该方法的有效性。
To achieve the goal of high wind power penetration in future smart grids, economic energy management accounting for the stochastic nature of wind power is of paramount importance. Multi-period economic dispatch and demand-side management for power systems with multiple wind farms is considered in this paper. To address the challenge of intrinsically stochastic availability of the non-dispatchable wind power, a chance-constrained optimization problem is formulated to limit the risk of supply-demand imbalance based on the loss-of-Ioad probability (LOLP). Since the spatio-temporal joint distribution of the wind power generation is intractable, a novel scenario approximation technique using Monte Carlo sampling is pursued. Enticingly, the problem structure is leveraged to obtain a sample-size-free problem formulation, thus making it possible to accommodate a very small LOLP requirement even with a long scheduling time horizon. Finally, to capture the temporal and spatial correlation among power outputs of multiple wind farms, an autoregressive model is introduced to generate the required samples based on wind speed distribution models as well as the wind-speed-to-power-output mappings. Numerical results are provided to corroborate the effectiveness of the novel approach.