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首页> 外文期刊>Electric power systems research >Day-ahead and intra-day optimization for energy and reserve scheduling under wind uncertainty and generation outages
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Day-ahead and intra-day optimization for energy and reserve scheduling under wind uncertainty and generation outages

机译：在风不确定性和一代中断下的能源和储备调度的日期和日期优化

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摘要

The procurement of reserves becomes indispensable to hedge against the increasing penetration of renewable energy resources with variable nature and limited predictability. This paper presents a multi-time-scale optimal scheduling framework within the context of co-optimized electricity markets for energy and reserve, wherein the supply-side and demand-side resources are coordinated in the normal case and uncertainty cases to accommodate the variations of prediction errors over time and enhance the system cost-effectiveness and reliability. The optimization process consists of two phases, day-ahead planning and intra-day adjustment. In the day-ahead planning, a robust energy and reserve dispatch model is put forward considering continuous load/wind power and discrete generator state uncertainties, which can be solved efficiently by the combination of column-andconstraint generation (C&CG) algorithm and Karush-Kuhn-Tucker (KKT) optimality conditions. The optimal results provide robust operating plans for the intra-day dispatch. In the intra-day adjustment, the source-load deviations caused by various uncertainties can be compensated by the proposed rolling stochastic optimization (SO) model with quick-start units, which modulates the energy and reserve contributions of units and demand-side resource providers (DRPs). The superiority and validity of the proposed models are verified in case studies.
机译：储备采购是对对冲对冲以可变性质和可预测性有限的可再生能源的渗透率的对冲不可或缺。本文在能量和储备的共同优化电力市场的背景下提出了一种多时间尺度的最佳调度框架，其中在正常情况下，供应方和需求侧资源在正常情况下和不确定性情况下协调以适应变化随着时间的推移预测误差，提高系统成本效益和可靠性。优化过程包括两个阶段，前方规划和日内调整。在前方规划中，考虑到连续负载/风电和离散发生器状态的不确定性，提出了一种强大的能量和储备调度模型，其可以通过柱和混合生成（C＆CG）算法和Karush-Kuhn的组合有效地解决-Tucker（KKT）最优性条件。最佳结果为日内派遣提供了强大的运营计划。在日内调整中，通过使用快速启动单元的建议的滚动随机优化（SO）模型可以补偿由各种不确定性引起的源负荷偏差，这是速度启动单元，它调制单位和需求侧资源提供商的能量和储备贡献（DRPS）。在案例研究中核实了所提出的模型的优越性和有效性。

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