Stochastic Optimal Energy Storage Management for Energy Routers Via Compressive Sensing

Abstract

The functionality of energy routing among microgrids is becoming increasingly important with the progress of deploying smart power systems all over the world. For higher energy routing performance and better renewable energy integration, a new type of electrical device, called energy router (ER), is being developed as a part of the infrastructure of the future energy Internet (EI). Generally, the long-term operation of ERs requires an effective energy management scheme for the energy storage inside these devices. In this article, considering the randomness of power generation by renewable energy sources and the stochastic power usage of loads in EI scenario, the compressive sensing is adopted for the solution to the nonlinear energy storage management problem which is essential for the design of ERs. The compressive sensing method used in this article is proven to be more efficient than the conventional Monte–Carlo methods and polynomial chaos expansion method, and the performance of the proposed method is evaluated with numerical examples.