Matching a Desirable Generation Pattern for Large-scale Wind Farm with Autonomous Energy Storage Control Strategy

The major obstacle toward wind farm integration is power mismatch between wind output and grid desirable generation due to wind uncertainty and negative effect on peak load regulation. Storage can be used as assistance to reduce this mismatch. Needed storage capacity for full level of assistance is acceptable only for small-scale wind farm with qualified matching requirement. But when it comes to large-scale one, capacity is huge and unacceptable. In this paper, a matching desirable generation(MDG) pattern with autonomous storage control strategy is proposed to quantify the level of storage assistance. The minimum storage capacity to achieve a qualified matching requirement, i. e. needed capacity merely to reduce uncertainty and no peak load regulation, is presented. Mathematical model of wind-storage sizing problem is established to seek a trade-off between electricity profit and storage investment with matching probability constrain. Markovian steady-state sizing method is proposed to reduce computational burden. Numerical tests show that matching probability can achieve 96% with almost no benefit reduction when assisted storage capacity is only about 25% of average wind power, and proposed sizing method is fast and accurate due to the ability of handling large dataset.