Abstract
Minimum energy storage (ES) and spinning reserve (SR) for day-ahead power system scheduling with high wind power penetration is significant for system operations. A chance-constrained energy storage optimization model based on unit commitment and considering the stochastic nature of both the wind power and load demand is proposed. To solve this proposed chance-constrained model, it is first converted into a deterministic-constrained model using p-efficient point theory. A single stochastic net load variable is developed to represent the stochastic characteristics of both the wind power and load demand for convenient use with the p-efficient point theory. A probability distribution function for netload forecast error is obtained via the Kernel estimation method. The proposed model is applied to a wind-thermal-storage combined power system. A set of extreme scenarios is chosen to validate the effectiveness of the proposed model and method. The results indicate that the scheduled energy storage can effectively compensate for the net load forecast error, and the increasing wind power penetration does not necessarily require a linear increase in energy storage.
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