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Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control

机译:基于内点法的模型预测控制的风/电储能混合动力系统调度

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The application of large scale energy storage makes wind farms more dispatchable, which lowers operating risks to the grid from interconnected large scale wind farms. In order to make full use of the flexibility and controllability of energy storage to improve the schedulability of wind farms, this paper presents a rolling and dispatching control strategy with a battery energy storage system (BESS) based on model predictive control (MPC). The proposed control scheme firstly plans expected output, i.e., dispatching order, of a wind/battery energy storage hybrid system based on the predicted output of the wind farm, then calculates the order in the predictive horizon with the receding horizon optimization and the limitations of energy storage such as state of charge and depth of charge/discharge to maintain the combination of active output of the wind farm and the BESS to track dispatching order at the extreme. The paper shows and analyses the effectiveness of the proposed strategy with different sizes of capacity of the BESS based on the actual output of a certain actual wind farm in the northeast of China. The results show that the proposed strategy that controls the BESS could improve the schedulability of the wind farm and maintain smooth output of wind/battery energy storage hybrid system while tracking the dispatching orders. When the capacity of the BESS is 20% or the rated capacity of the wind farm, the mean dispatching error is only 0.153% of the rated capacity of the wind farm.
机译:大型储能的应用使风电场更易于调度,从而降低了相互连接的大型风电场对电网的运行风险。为了充分利用储能的灵活性和可控性来提高风电场的可调度性,提出了一种基于模型预测控制(MPC)的电池储能系统(BESS)的滚动和调度控制策略。所提出的控制方案首先基于风电场的预测输出来计划风/电池储能混合系统的预期输出,即调度顺序,然后在后退水平优化和限制条件下计算预测水平中的顺序。能量存储,例如充电状态和充电/放电深度,以保持风电场的有效输出和BESS的组合,以在极端情况下跟踪调度顺序。本文基于中国东北某特定风电场的实际产量,展示并分析了该策略在不同规模的BESS容量下的有效性。结果表明,所提出的控制BESS的策略可以提高风电场的可调度性,并在跟踪调度顺序的同时保持风能/电池储能混合系统的平稳输出。当BESS的容量为风电场的额定容量的20%时,平均调度误差仅为风电场的额定容量的0.153%。

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