A new prediction model of battery and wind-solar output in hybrid power system
A new prediction model of battery and wind-solar output in hybrid power system
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DOI：
10.1007/s12652-017-0600-7
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发表时间：
2019-01-01
影响因子：
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通讯作者：
Ghadimi, Noradin
中科院分区：
文献类型：
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作者：
Mirzapour, Farzaneh;Lakzaei, Mostafa;Ghadimi, Noradin
本文提出了风电和太阳能发电的短期功率预测，以评估各生产部件的可用输出功率。在该模型中，所提出的基于风能-太阳能的混合动力系统采用了铅酸蓄电池。因此，在预测输出功率之前，将介绍一种简单的数学方法来模拟独立混合风力-太阳能发电系统中铅酸电池的行为。然后，将所提出的预测问题通过电池的荷电状态（SOC）作为约束状态进行评估。提出的预测模型包括特征选择过滤器、基于神经网络的混合预测引擎和智能进化算法。该方法不仅可以将电池的荷电状态维持在合适的范围内，而且可以减少风力发电机组和光伏组件的开关次数。该方法的有效性已在实际工程数据中得到了应用。通过数值分析，验证了所提方法的有效性。
In this paper short term power forecast of wind and solar power is proposed to evaluate the available output power of each production component. In this model, lead acid batteries used in proposed hybrid power system based on wind-solar power system. So, before the predicting of power output, a simple mathematical approach to simulate the lead-acid battery behaviors in stand-alone hybrid wind-solar power generation systems will be introduced. Then, the proposed forecast problem will be evaluated which is taken as constraint status through state of charge (SOC) of the batteries. The proposed forecast model includes a feature selection filter and hybrid forecast engine based on neural network (NN) and an intelligent evolutionary algorithm. This method not only could maintain the SOC of batteries in suitable range, but also could decrease the on-or-off switching number of wind turbines and PV modules. Effectiveness of the proposed method has been applied over real world engineering data. Obtained numerical analysis, demonstrate the validity of proposed method.