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Smart Charging of Electric Vehicles Considering Stochastic Aspects of Renewable Energy Availability


F. Schmidt

Master Thesis, FS13 (10229)

The main goal of this thesis is to model and solve a lowest cost charging schedule computation for a pool of electric vehicles considering stochastic aspects of renewable energy availability. As an example, wind speed is considered to be Weibull-distributed and integrated into the optimization problem in form of wind power generated by a wind turbine. Since the problem is driven by randomness, the expected value of the charging cost is minimized. Distinguishing between a discrete and a continuous distribution of wind speed it is shown that a discretization can approximate the continuous case suciently well such that the problem can be formulated as a linear program. As a result it is possible to assume any kind of probability distribution in a relatively simple way. In a real world application the problem could be solved in a receding horizon control manner for a 24 hour time horizon divided into 96 time slots with a length of 15 minutes. This would require a suciently short computation time such that a new schedule can be calculated in each time slot. As a matter of fact with increasing number of vehicles the linear program grows and solving the problem globally can take too much time. This work proposes a solution technique referred to as \Sum Passing" that decomposes the problem into several smaller linear programs which can be solved in parallel. It is shown that even for a large vehicle pool size a short calculation time can be achieved. Consequently the running time of the algorithm depends only on the computer and the number of CPU-cores available for parallel computation.


Type of Publication:

(12)Diploma/Master Thesis

K. Margellos

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2013:IFA_4513
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