Decentralized Energy Management and Robust Decisions for Networked Microgrids (GR-17-14)

Principal Investigator: Dr. Lingfeng Wang

A growing amount of distributed energy resources is being integrated into both electricity distribution systems (DS) and microgrids (MGs). The efficient coordination between DS and MGs becomes increasingly critical for DS operators and MG operators to achieve effective energy management, considering the high uncertainties contributed by the intermittency of renewable energy resources (e.g., wind and solar) and random variations of load demands. In this project, a novel, decentralized energy management framework will be developed to efficiently coordinate the power exchange between DS and MGs based on a fully decentralized optimization algorithm termed alternating direction method of multipliers (ADMM). Individual energy management model for each entity (DS or MGs) will be built based on the two-stage robust optimization theory in order to address all the potential uncertainties. Mathematically, the formulated problem will be handled with a second order cone programming method based on a relaxed distflow model. Moreover, the developed robust model will be solved by the column and constraint generation (CCG) algorithm, where cutting planes are introduced to ensure the exactness of second order cone relaxation. The proposed method will be tested on a number of IEEE test systems and practical systems with multiple interconnected MGs. If successful, this work will be highly beneficial to more effectively coordinating interconnected microgrids and distribution grids to ensure higher renewable energy integration, higher economic efficiency, and more reliable power system operations.


Posted on

July 1, 2017

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