A data-adaptive robust load restoration algorithm for networked microgrids within a transactive energy framework

• The novel transactive energy framework for the load restoration of networked MGs captures the private nature of MGs. • A parallel algorithm for load restoration among MGs improves computational efficiency. • A corner-segmentation convex hull set reduces computational complexity and conservatism. • The modified linearized penalty function formulates the robust load restoration model without compromising accuracy. To consider the private nature of microgrids (MGs) in load restoration for networked MGs under uncertainty, a data-adaptive robust algorithm is proposed within a transactive energy framework. A novel transactive framework for load restoration of networked microgrids (MGs) is developed to capture the private nature of MGs through retail electricity prices and network tariffs. A parallel algorithm based on the analytic target cascading (ATC) method is proposed. It enhances computational efficiency by simultaneously solving for boundary variables in each iteration. A data-adaptive robust load restoration model is developed. The model is based on a corner-segmentation convex hull set to address computational complexity and reduce conservatism. Numerical simulations conducted on a practical distribution network demonstrate that the proposed algorithm achieves higher efficiency and accuracy than existing methods.