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多时间尺度下电氢耦合系统的实时运行调度优化

Real-Time Operation Scheduling Optimization of Electric-Hydrogen Coupling System Under Multiple Time Scales

  • 摘要:
    目的 在新型电力系统快速发展形势下,电氢耦合系统逐渐呈现多时间尺度、多重不确定性、高维多层次、非线性等特征,对运行调度决策提出了更高的要求。因此如何在应对可再生能源出力扰动、负荷变化、设备动态特性等情况的同时,保证系统的优化调度结果质量至关重要。
    方法 开展多时间尺度下电氢耦合系统的实时运行调度优化,通过建立基于模型预测控制的日前-日内优化决策模型,结合设备运行特性与电网交互约束限制,形成日前计划、日内滚动调整的多时间尺度调度优化框架,提供多时间尺度下的闭环优化控制策略,实现考虑系统内灵活性资源的实时调度控制。
    结果 结果表明,日前计划基于较大时间尺度与较低预测精度数据,对不确定性变量波动估计不足,结果偏理想化与均质化,存在短时供需不平衡风险,与日内条件下的调度结果有较大差异。在日内修正下,电网购电、储能作为灵活性资源成为调控的关键设备,均值及峰值变化幅度最大可达23.3%,且在日内修正下,对于风光突变情况做到及时应对。
    结论 所建立的日前日内多时间尺度优化调度模型,基于日前优化调度的结果,在日内滚动优化中对可再生能源出力及用户负荷进行精确预测并滚动修正单元出力,有效抑制了日前日内偏差所带来的功率波动,还可以使运行调度过程更加灵活机动,避免了按照单一固定时间尺度调度结果所可能带来的供需不平衡问题,提高了系统的运行稳定性。

     

    Abstract:
    Objective With the rapid development of new power systems, the electrc-hydrogen coupling system gradually presents the characteristics of multiple time scales, multiple uncertainties, high-dimensional multiple levels, nonlinearity, etc., which puts forward higher requirements for operation scheduling decision-making. Therefore, it is very important to ensure the quality of optimal scheduling results while dealing with the renewable energy output disturbance, load changes and equipment dynamic characteristics.
    Method In this study, real-time operation scheduling optimization of the electric-hydrogen coupling system under multi-time scales was carried out. By establishing a day-ahead-intraday optimization decision-making model based on model predictive control and combining the equipment operation characteristics with the grid interaction constraints, a multi-time scale scheduling optimization framework with day-ahead planning and intraday rolling adjustment was formed to provide a closed-loop optimization control strategy under multi-time scales. This realized real-time scheduling control that considered flexible resources in the system.
    Result The results show that the day-ahead planning, based on large time scale, low prediction accuracy data, insufficiently estimated the fluctuation of uncertain variables, leading to the idealization and homogenization of the results. At the same time, there is the risk of short-term supply and demand imbalance, which is quite different from the scheduling results under intraday conditions. Under intraday correction, grid electricity purchase and energy storage, as flexible resources, have become key equipment for regulation. The maximum variation range of the average and peak values can reach 23.3%. Moreover, under intraday correction, timely responses can be made to sudden changes in wind and solar power.
    Conclusion The established multi-time scale optimization scheduling model, based on the results of day-ahead optimization scheduling, accurately predicts the renewable energy output and user energy demand in intraday rolling optimization and adjusts the unit output. It effectively suppresses the power fluctuation caused by the day-ahead and intraday deviations and can also make the operation scheduling process more flexible and mobile. It avoids the problem of supply and demand imbalance that may be caused by scheduling results based on a single fixed time scale and improves the operational stability of the system.

     

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