Collective Effects and Performance of Algorithmic Electric Vehicle Charging Strategies
| Autor: | Richard J. Gibbens, Miroslav Gardlo, Frank Kelly, Rui Carvalho, Lubes Buzna |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization business.product_category Grid network Computer science 020209 energy Process (computing) 02 engineering and technology Proportionally fair 7. Clean energy Weighting Dynamic simulation 020901 industrial engineering & automation Optimization and Control (math.OC) Component (UML) Electric vehicle 0202 electrical engineering electronic engineering information engineering FOS: Mathematics business Mathematics - Optimization and Control Interior point method |
| Zdroj: | COMPENG (2018). 2018 IEEE Workshop on Complexity in Engineering (COMPENG), 10-12 October 2018, Florence. Piscataway, NJ: IEEE, pp. 1-7 |
| DOI: | 10.48550/arxiv.1810.01766 |
| Popis: | We combine the power flow model with the proportionally fair optimization criterion to study the control of congestion within a distribution electric grid network. The form of the mathematical optimization problem is a convex second order cone that can be solved by modern non-linear interior point methods and constitutes the core of a dynamic simulation of electric vehicles (EV) joining and leaving the charging network. The preferences of EV drivers, represented by simple algorithmic strategies, are conveyed to the optimizing component by real-time adjustments to user-specific weighting parameters that are then directly incorporated into the objective function. The algorithmic strategies utilize a small number of parameters that characterize the user's budgets, expectations on the availability of vehicles and the charging process. We investigate the collective behaviour emerging from individual strategies and evaluate their performance by means of computer simulation. Comment: 7 pages, 4 figures, COMPENG conference |
| Databáze: | OpenAIRE |
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