Control scheme to extend lifetime of BESS for assisting wind farm to track power generation plan based on wind power feature extraction

Abstract

In allusion to the issues of high loss of lifetime and neglecting scheduling potential, a control scheme to extend BESS lifetime for assisting wind farm to track power generation plan based on wind power feature extraction through improved whale optimization algorithm (IWOA) optimized swing door trending (IWOA‐SDT) is proposed. Firstly, an IWOA is designed to optimize the compression offset of the swing door trending algorithm. The preliminary wind power feature is extracted by IWOA‐SDT and is further corrected with actual output of wind power to acquire the corrected wind power feature. Secondly, BESS is divided into two groups and the second battery group is subdivided into three battery clusters, and their capacities are configured by the deviation between preliminary feature and forecasted power. The grouping state of battery units is dynamically adjusted based on devised dynamic grouping method at the corresponding time of corrected wind power feature. The power coordinative allocation approach of the two battery groups is designed. A power coordinative allocation strategy for battery groups is devised to obtain the power regulation signals of two battery groups. The power allocation in battery units of the first group is calculated by the number of the units, and the double‐layer power allocation in battery units of the second group coordinated with multiple rules is devised. Finally, the proposed control scheme is implemented in a wind farm to track the power generation plan, and the results show that the wind farm with BESS can accurately track the power generation plan, the service life of BESS is significantly extended and the scheduling potential is sufficient for the next period.

Resilience analysis and improvement strategy of microgrid system considering new energy connection

“…Step 5: Construct an optimization model for the resilience improvement strategy of the microgrid system, as shown in Eq (8), and perform optimization calculations based on the control parameters of each component to obtain an optimization method for resilience improvement. The control center sends control instructions to implement the resilience improvement method.…”

“…Through the calculation of the established optimization control model (8), the adjusted parameters of some components are shown in Table 4.…”

“…In the face of increasingly severe challenges, the academic and engineering communities at home and abroad have gradually attached importance to the study of resilience issues in microgrid systems. Against this backdrop, the concept of power system resilience emerged and achieved some good results [5][6][7][8][9][10].…”