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Risk-based Reserve Coordinative Unit Commitment for a Large-scale Wind-storage System

机译：大型储风系统基于风险的储备协调单位承诺

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摘要

The increasingly high integration of wind power into power systems will further increase the power uncertainty and make it difficult to calculate the requirement of the reserve. In this article, a risk-based reserve optimization approach is proposed to quantitatively evaluate the reserve requirement of a large-scale wind-storage system. Conditional value-at risk (CVaR) is adapted to calculate the risk reserve managing the uncertainty of wind generation; this risk reserve achieves the optimal risk without sacrificing system reliability. An energy storage system (ESS) is employed to undertake the role of reserve transfer (RT) by cooperating with wind generation and conventional thermal units in the decision-making of unit commitment (UC); their cooperation and coordination can be achieved by building a bilevel optimization model with a day-ahead risk-constrained unit commitment model and a real-time risk reserve adjustment model. By using the duality principle and the big-M method, the formulations are converted into a mixed integer linear programing problem (MILP) that is solved using a column and constraint generation algorithm (C&CG). The model is tested on the six-bus system and the IEEE 118-bus system. The simulation results show that the ESS can transfer the reserve capacity of the thermal generator and improve the ability to accommodate the uncertainty of wind power generation, thereby demonstrating the effectiveness of the proposed methodology.
机译：风能在电力系统中的集成度越来越高，这将进一步增加电力的不确定性，并使其难以计算备用量的要求。本文提出了一种基于风险的储量优化方法，以定量评估大型储能系统的储量需求。条件风险值（CVaR）用于计算管理风力发电不确定性的风险准备金；这种风险储备可在不牺牲系统可靠性的情况下实现最佳风险。储能系统（ESS）通过与风力发电和常规热力单元合作，在机组承诺（UC）的决策中承担起储备转移（RT）的作用；它们的合作与协调可以通过建立一个具有每日风险约束的单位承诺模型和实时风险准备金调整模型的双层优化模型来实现。通过使用对偶原理和big-M方法，将公式转换为使用列和约束生成算法（C＆CG）求解的混合整数线性规划问题（MILP）。该模型在六总线系统和IEEE 118总线系统上进行了测试。仿真结果表明，ESS可以转移热力发电机的备用容量，提高适应风力发电不确定性的能力，从而证明了该方法的有效性。

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