Dong Peng
ABSTRACT
Battery energy storage systems (BESS) are used for peak shaving, valley filling and frequency regulation due to their flexible power throughput characteristics. However, there are few economic studies that consider the cost and benefits of energy storage. In this paper, a grid-side peak shaving and valley filling control strategy based on BESS is established. In addition, a mathematical model for the economic evaluation of the participating grid-side BESS is developed and solved by applying various intelligent algorithms. Subsequently, the peak-shaving and valley-filling control strategies obtained using multiple algorithms are used as training data for deep learning long and short-term memory networks (LSTM). The trained network can be predicted to get quality and efficient grid scheduling control strategies under such models. The model is validated by a simulation example. In this example, the optimal capacity allocation of the BESS is derived through cost and benefit analysis. Based on this capacity, a corresponding control strategy is developed and peak shaving and valley filling are effectively realized.
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Index Terms
- Economic Analysis Model of Battery Energy Storage System based on Long-short-term Memory Neural Network Algorithm
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