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Optimal Offering and Operating Strategies for Wind-Storage System Participating in Spot Electricity Markets with Progressive Stochastic-Robust Hybrid Optimization Model Series

机译：利用渐进随机稳健的混合优化模型参与现货电信市场的蓄电系统最优提供和运行策略

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摘要

With the increase of wind power installed capacity and the development of energy storage technologies, it is gradually accepted that integrating wind farms with energy storage devices to participate in spot electricity market (EM) is a promising way for improving wind power uncertainty accommodation and bringing considerable profit. Hence, research on reasonable offering and operating strategies for integrated wind farm-energy storage system (WF-ESS) under spot EM circumstances has important theoretical and practical significance. In this paper, a newly progressive stochastic-robust hybrid optimization model series is proposed for yielding such strategies. In the day-ahead stage, day-ahead and balancing prices uncertainties are formulated by applying joint stochastic scenarios, and real-time available wind power uncertainties are modeled by using the seasonal auto-regression (AR) based dynamic uncertainty set. Then, the first model of this model series is established and utilized for cooptimizing both the day-ahead offering and nominal real-time operating strategies. In the balancing stages, wind power uncertainty set and balancing prices stochastic scenarios are dynamically updated with the newly realized data. Then, each model from the remaining of this model series is established and utilized period by period for obtaining the optimal balancing/real-time offering/operating strategies adjusted from the nominal ones. Robust optimization (RO) in this progressive framework makes the operation of WF-ESS dynamically accommodate wind power uncertainties while maintaining relatively low computational complexity. Stochastic optimization (SO) in this progressive framework makes the WF-ESS avoid pursuing profit maximization strictly under the worst-case scenarios of prices uncertainties. Moreover, by adding a risk-aversion term in form of conditional value at risk (CVaR) into the objective functions of this model series, the optimization models additionally provide flexibility in reaching a trade-off between profit maximization and risk management. Simulation and profit comparisons with other existing methods validate the scientificity, feasibility, and effectiveness of applying our proposed model series.
机译：随着风电装机容量的增加和能量存储技术的发展，逐步接受与能量存储设备集成到能量存储设备，参与现货电力市场（EM）是改善风力不确定性住宿并带来相当大的通用方式利润。因此，在现场EM情况下，对综合风电场 - 能量存储系统（WF-ESS）合理提供和运营策略具有重要的理论和实践意义。本文提出了一种新的逐步随机稳健的混合优化模型系列，用于产生这种策略。在前面的舞台上，通过应用联合随机场景来制定前一天和平衡价格，通过使用基于季节性回归（AR）的动态不确定性集来建立实时可用的风电不确定性。然后，该模型系列的第一个模型建立并用于COOVIMIZED，既有日子推销和名义的实时操作策略。在平衡阶段，风电不确定性集和平衡价格随机方案随着新实现的数据而动态更新。然后，从剩余的该模型系列中的每个模型都是通过期间建立和利用的，以获得从标称值调整的最佳平衡/实时提供/操作策略。该渐进框架中的鲁棒优化（RO）使WF-ES的操作动态地容纳风力不确定性，同时保持相对较低的计算复杂性。随机优化（SO）在这一进步框架中，WF-ESS在价格不确定性的最坏情况下严格避免严格追求利润最大化。此外，通过以风险（CVAR）的条件值形式添加风险厌恶项，进入该模型系列的客观函数，优化模型另外提供了在利润最大化和风险管理之间达到权衡的灵活性。与其他现有方法的仿真和利润比较验证了应用我们提出的模型系列的科学性，可行性和有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第15期|8.1-8.19|共19页
  • 作者

    Wang Yuwei; Zhao Huiru; Li Peng;

  • 作者单位

    North China Elect Power Univ Dept Econ Management Baoding 071003 Peoples R China;

    North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China;

    State Grid Henan Econ Res Inst Zhengzhou 450052 Henan Peoples R China;

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