Stochastic Optimization for Energy Management in Power Systems With Multiple Microgrids

This paper is motivated by a power system with one main grid (arbiter) and multiple microgrids (MGs) (agents). The MGs are equipped to control their local generation and demands in the presence of uncertain renewable generation and heterogeneous energy management settings. We propose an extension to...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2019-01, Vol.10 (1), p.1068-1079
Main Authors: Wang, Shasha, Gangammanavar, Harsha, Eksioglu, Sandra D., Mason, Scott J.
Format: Article
Language:English
Subjects:
Air conditioners
Air conditioning
Algorithms
Computer storage devices
demand response
Distribution functions
Electric appliances
Electric power grids
Electric power systems
Energy management
Household appliances
Mathematical model
Microgrids
multi-agent stochastic optimization
Optimization
Probability distribution functions
Programming
Sequential sampling
Statistical analysis
Statistical methods
stochastic decomposition
Stochastic processes
Ventilation
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Summary:This paper is motivated by a power system with one main grid (arbiter) and multiple microgrids (MGs) (agents). The MGs are equipped to control their local generation and demands in the presence of uncertain renewable generation and heterogeneous energy management settings. We propose an extension to the classical two-stage stochastic programming model to capture these interactions by modeling the arbiter's problem as the first-stage master problem and the agent decision problems as second-stage subproblems. To tackle this problem formulation, we propose a sequential sampling-based optimization algorithm that does not require a priori knowledge of probability distribution functions or selection of samples for renewable generation. The subproblems capture the details of different energy management settings employed at the agent MGs to control heating, ventilation, and air conditioning systems, home appliances, industrial production, plug-in electrical vehicles, and storage devices. Our computational experiments conducted on the U.S. western interconnect (WECC-240) data set illustrate that the proposed algorithm is scalable and the solutions are statistically verifiable. Our results also show that the proposed framework can be used as a systematic tool to gauge: 1) the impact of energy management settings in efficiently utilizing renewable generation and 2) the role of flexible demands in reducing system costs.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2017.2759159