Optimal Scheduling of an Isolated Microgrid With Battery Storage Considering Load and Renewable Generation Uncertainties

By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed in this paper for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 66; no. 2; pp. 1565 - 1575
Main Authors: Li, Yang, Yang, Zhen, Li, Guoqing, Zhao, Dongbo, Tian, Wei
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Batteries
Chance-constrained programming (CCP)
Communications systems
Confidence intervals
discretized step transformation (DST)
distributed energy resources (DERs)
Distributed generation
Energy storage
Hybrid systems
Integer programming
isolated microgrids
Linear programming
Microgrids
Mixed integer
Modelling
Operating costs
Optimal scheduling
Probabilistic logic
Programming
Research facilities
Scheduling
Spinning
spinning reserves
Statistical analysis
uncertainties
Uncertainty
Wind speed
ISSN:0278-0046, 1557-9948
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Summary:By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed in this paper for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming formulation in general algebraic modeling system (GAMS) via a proposed discretized step transformation approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can achieve a tradeoff between reliability and economy. The test results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2018.2840498