Parallel and Distributed Optimization Method With Constraint Decomposition for Energy Management of Microgrids

Energy management in power systems is a thorny optimization problem. With the sizes of systems rising, centralized optimization methods are restricted by their complexities of communications, while distributed optimization methods have emerged as a powerful tool for dealing with increasingly complex...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2021-11, Vol.12 (6), p.4627-4640
Main Authors: Li, Qiang, Liao, Yuexi, Wu, Kunming, Zhang, Leiqi, Lin, Jiayang, Chen, Minyou, Guerrero, Josep M., Abbott, Derek
Format: Article
Language:English
Subjects:
Complex systems
Convergence
Convex functions
Cost function
Decomposition
demand response
Distributed generation
distributed optimization
Economics
Energy management
Linear programming
Local optimization
Microgrids
multi-agent systems
Optimization
Optimization methods
Power dispatch
Symmetric matrices
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Summary:Energy management in power systems is a thorny optimization problem. With the sizes of systems rising, centralized optimization methods are restricted by their complexities of communications, while distributed optimization methods have emerged as a powerful tool for dealing with increasingly complex systems. However, convergence rates of some widely used distributed optimization methods, such as the standard alternating direction method of multipliers (ADMM), still have room for improvement. In this paper, a parallel and distributed optimization method for energy management of microgrids (MGs) is proposed to boost the convergence rate without sacrificing the accuracy of the optima, in which agents calculate, exchange and update in parallel. At first, a decomposition method is presented, where the objective functions and constraints of an original optimization problem with separable variables are decomposed into local objective functions and constraints for agents, which is the key to our method. Further, agents solve their local optimization problems independently and then exchange determined optima with their neighbors. Finally, the method is evaluated to solve economic dispatch with demand response for microgrids. The simulation results show that compared to the standard ADMM, for a given accuracy, the number of iterations in our method is only one third or even less than that of ADMM. Furthermore, our method can minimize the cost functions of distributed generation on supply side and maximize the profit functions of flexible loads on the demand side.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2021.3097047