Robust Optimal Dispatch of AC/DC Hybrid Microgrids Considering Generation and Load Uncertainties and Energy Storage Loss
Uncertainties from multiple generation resources and loads have introduced tremendous challenges to the optimal dispatch of microgrids. This paper presents a novel two-stage min-max-min robust optimal dispatch model for a representative islanded AC/DC hybrid microgrid that faces uncertainties in renewable energy generation and customer loads. The first stage of the model determines the startup/shutdown state of the diesel engine generator and the operating state of the bi-directional converter of the microgrid. Then, the second stage optimizes the power dispatch of individual units in the microgrid. A new linearized equipment cost model is developed, counting for the degradation of energy storage. The use of this linear model helps maintain the linearity of objective function without compromising the solution accuracy. The column-and-constraint generation algorithm is implemented to efficiently obtain a robust dispatching plan for the microgrid, which minimizes the daily operating cost in the worst-case scenario. A case study and sensitivity analyses further demonstrate the rationale and the unique capability of the proposed model for planning the operation of AC/DC hybrid microgrids.
B. Zhao et al., "Robust Optimal Dispatch of AC/DC Hybrid Microgrids Considering Generation and Load Uncertainties and Energy Storage Loss," IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 5945-5957, Institute of Electrical and Electronics Engineers (IEEE), Nov 2018.
The definitive version is available at https://doi.org/10.1109/TPWRS.2018.2835464
Engineering Management and Systems Engineering
Intelligent Systems Center
National Science Foundation of China
Keywords and Phrases
AC/DC Hybrid Microgrid; Energy Storage Loss; Optimal Dispatch; Robust Optimization; Uncertainty
International Standard Serial Number (ISSN)
Article - Journal
© 2018 Institute of Electrical and Electronics Engineers Inc. (IEEE), All rights reserved.
01 Nov 2018