A data-driven approach for microgrid distributed generation planning under uncertainties

The increasing demand for power system decarbonization and resilience raises the necessity of incorporating the renewable distributed generation (DG) into the microgrid planning. The complexity of the microgrid renewable DG planning largely roots from the intermittent wind and solar energy and load variations throughout the planning period. This paper proposes a novel two-stage data-driven adaptive robust distributed generation planning (DDARDGP) framework considering both grid-connected and islanded modes of microgrids, wherein the overall system cost is minimized. By leveraging the spatio-temporal property of historical weather and grid information, a compact uncertainty set is developed based on a data-driven Bayesian nonparametric approach. The problem is further solved by a modified column and constraint generation (CC&G) algorithm. In the study, the effectiveness of the proposed framework is demonstrated using a modified IEEE 33-bus test system. The case study considers the optimal generation sizing, allocation and mixtures. The simulation results confirm that the proposed data-driven uncertainty set adapts well to the increase of data dimensions and solves the over-conservatism issue, leading to 34.14% reduction in uncertainty estimation compared with the traditional budget uncertainty set. Accordingly, the total cost can achieve a $23,185 reduction under the proposed DDARDGP framework.