Multi-stage hybrid energy management strategy for reducing energy abandonment and load losses among multiple microgrids

This paper proposes a multi-stage hybrid energy management strategy for multiple microgrids (MMGs) to reduce energy abandonment and load losses. The proposed energy management model consists of three stages including a decentralized autonomy stage, a coordinated operation stage, and a reserve reallocation stage, respectively. First, in the decentralized autonomy stage, a day-ahead energy management model is established with the objective of minimizing the comprehensive management cost of individual microgrids. Especially, the uncer-tainty from renewable energy and load demands are quantified by interval forecast algorithm, phase space reconstruction technique, machine learning, and kernel density estimation, which simplifies the forecasting processes while capturing multivariate data more comprehensively. Second, the coordinated operation stage aims to encourage the power interaction among MMGs to achieve maximize the benefit of MMGs while sup-pressing the power fluctuations of the tie line between the MMGs and the main grid. Then, in the reserve reallocation stage, taking the actual values of energy abandonment and load losses of a week before the man-agement day as the dataset, machine learning algorithms are applied to predict energy abandonment and load losses. Meanwhile, the actual energy abandonment and load losses are reduced by dispatching reserve resources. Finally, simulations on an MMGs system containing four microgrids are conducted to testify the rationality and validity of the proposed energy management strategy.