Distributed Convex Optimization for Electric Vehicle Aggregators

One of the main challenges for electric vehicle (EV) aggregators is the definition of a control infrastructure that scales to large EV numbers. This paper proposes a new optimization framework for achieving computational scalability based on the alternating directions method of multipliers, which al...

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Vydané v:IEEE transactions on smart grid Ročník 8; číslo 4; s. 1852 - 1863
Hlavní autori: Rivera, Jose, Goebel, Christoph, Jacobsen, Hans-Arno
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
ADMM
Benchmark testing
Charging
Computational geometry
Convex functions
Convexity
Cost function
distributed computing
Distributing
Electric power distribution
Electric vehicles
Jacobian matrices
Multipliers
Optimization
Scalability
smart grid
Versatility
ISSN:1949-3053, 1949-3061
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Shrnutí:One of the main challenges for electric vehicle (EV) aggregators is the definition of a control infrastructure that scales to large EV numbers. This paper proposes a new optimization framework for achieving computational scalability based on the alternating directions method of multipliers, which allows for distributing the optimization process across several servers/cores. We demonstrate the performance and versatility of our framework by applying it to two relevant aggregator objectives: 1) valley filling; and 2) cost-minimal charging with grid capacity constraints. Our results show that the solving time of our approach scales linearly with the number of controlled EVs and outperforms the centralized optimization benchmark as the fleet size becomes larger.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2015.2509030