The cost of Energy Storage System (ESS) for frequency regulation is difficult to calculate due to battery's degradation when an ESS is in grid-connected operation. To solve this problem, the influence mechanism of actual operating conditions on the life degradation of Li-ion battery energy storage is analyzed. A control strategy of Li-ion ESS participating in grid frequency regulation is constructed and a cost accounting model for frequency regulation considering the effect of battery life degradation is established. The estimated operating life and annual average cost of the Li-ion ESS under different dead bands and SOC set-points are calculated. The case studies show that the estimated operating life of the Li-ion ESS under the actual operating condition differs significantly from the nominal life provided by the manufacturer under the standard condition and the full discharge mode. This paper provides an accurate costing method for the ESS participating in grid frequency regulation to help the promotion of the ESS to participate in the ancillary service market.

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Large scale wind power integration has a negative influence on the frequency response. Assistant measurement improves the frequency stability of power systems under high wind penetration. The Proportional Curtailment Strategy (PCS) for wind turbines provides a primary frequency reserve for power systems. To solve the worthless curtailed wind power, the PCS is used to improve the utilization of wind power curtailment. Then the wind turbine and the lithium battery Energy Storage System (ESS) provide primary frequency reserves together. Different control strategies of ESS have been proposed based on the different methods for selecting valid reserves. The economic benefits of different control strategies have been compared based on the same frequency regulation reserve. The optimal control strategy is the maximum method. The economic benefit of the maximum value method is ¥4,445,300.

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Being aware of the reliable margin of vital tie-lines, acting on the connection of power exporting area and power importing area, is significant to power systems. However, the high penetration of wind power causes fast variation of boundary limit parameters such as the available amount of power that can be transferred on the tie-lines, namely, total transfer capability (TTC), which may result in the inaccurate security assessment. Unfortunately, the traditional optimal power flow-based TTC model has computation burden for online applications. To address this problem, computational efficiency is improved via a datadriven TTC predictor based on an ensemble learning architecture in this paper. In the first stage, a daily profiles-based method including probabilistic sampling is proposed to simulate plenty of operation scenarios as data samples for ensemble training. Then, a hybrid feature selection approach, which is composed of the maximal information coefficient and nonparametric independence screening, is applied to determine the most correlative features to the objective variable. To enable the TTC predictor with high accuracy and generalization ability, a novel ensemble learning scheme for TTC predictor is constituted through clustering few adaptive hierarchical GA-based neural networks (AHGA-NNs predictor). At last, a modified New England test system is used to validate the proposed methodology. The results illustrate that combining with the appropriate feature selection, the presented ensemble learning has high performance on creating the accurate TTC predictor, which enables online secure margin monitoring for the vital tie-lines. INDEX TERMS Artificial neural networks, ensemble learning, feature selection, total transfer capability, wind power.