An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles.

Recent trends in the increasing penetration of renewable energy generation and the increasingly massive integration of electric vehicles (EVs) into multiple-microgrid (MMG) systems pose tremendous challenges to the effective energy management of these systems. However, the solutions of current energy management methods have the disadvantage of excessively limited robustness against uncertain fluctuations in renewable energy generation, and existing hierarchical EV scheduling methods are solved via an iterative process that is prohibitively time-consuming for real-time practical applications. This work addresses the abovementioned issues by constructing an improved data-driven polyhedral uncertainty set based on modified self-organizing feature map neural networks that perform data clustering. In addition, an iteration-free hierarchical framework is proposed for conducting the tri-layer energy management of MMGs with the massive integration of EVs. Specifically, the global positive power factor of EV aggregation is proposed to formulate a set of constraints into the upper-layer model of the tri-layer framework, which can guarantee the feasibility of the middle-and-lower layer models. The value of this factor is determined using a multi-objective optimization algorithm that makes a trade-off between the feasible range of the output of EV aggregators and the degree to which the trip demands of EVs are violated. The superiority and effectiveness of the proposed iteration-free robust hierarchical energy management method for MMGs are verified via simulations involving a practical MMG under high penetrations of renewable energy generation and massive integration of EVs. • Propose the iteration-free hierarchical scheduling method for addressing tri-layer energy management problems for MMGs with massive EVs. • The global positive power factor (GPPF) defined in the upper layer model can ensure the solution of the lower layer model is always feasible without iteration. • The GPPF value enables MG operators to flexibly select a tradeoff for the GPPF based on their operational experiences. • The novel time-series-enabled data-driven polyhedral uncertainty set can produce solutions with better robustness against uncertainties. [ABSTRACT FROM AUTHOR]