Networked Microgrids Planning Through Chance Constrained Stochastic Conic Programming

This paper presents a chance constrained stochastic conic program model for networked microgrids planning. Under a two-stage optimization framework, we integrate a multi-site microgrids investment problem and two sets of operational problems that correspond to the grid-connected and islanding modes,...

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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 10; no. 6; pp. 6619 - 6628
Main Authors: Cao, Xiaoyu, Wang, Jianxue, Zeng, Bo
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Benders decomposition
bilinear Benders decomposition
chance constrained stochastic program
Computational modeling
Constraints
Electric power distribution
Electric power grids
Electric power systems
Engineering
Investment
Islanding
Microgrids
Mixed integer
multi-site resource planning
Networked microgrids
Operational problems
Optimization
Planning
Power flow
Randomness
second-order conic program
Stochastic processes
Uncertainty
ISSN:1949-3053, 1949-3061
Online Access:Get full text
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Summary:This paper presents a chance constrained stochastic conic program model for networked microgrids planning. Under a two-stage optimization framework, we integrate a multi-site microgrids investment problem and two sets of operational problems that correspond to the grid-connected and islanding modes, respectively. To handle the uncertain nature of renewable energy generation and load variation, as well as the contingent islanding caused by external disruptions, stochastic scenarios are employed to capture randomness and a joint chance constraint is introduced to control the operational risks. A second-order conic program (SOCP) formulation is also utilized to accurately describe the AC optimal power flow (OPF) in operational problems. As the resulting mixed integer SOCP model is computationally difficult, we customize the bilinear Benders decomposition with non-trivial enhancement techniques to deal with practical instances. Numerical results on 5- and 69-bus networked microgrids demonstrate the effectiveness of the proposed planning model and the superior performance of our solution algorithm.
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USDOE Office of Electricity (OE)
OE0000842
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2019.2908848