[HEMS 1] Smart Charging of Electric Vehicles and Heat Pumps using Stochastic Optimization | BEAMS

[HEMS 1] Smart Charging of Electric Vehicles and Heat Pumps using Stochastic Optimization

Project information
Project type: 
Master thesis
Academic year: 
2017-2018
Status: 
Attributed
Research unit: 
Electrical Energy
BEAMS supervisors
Academic promoter
Supervisor
Student(s)
Tom Vandenbroucke

Plug-in electric vehicles (PEVs) and heat pumps (HP) are growing in popularity as more efficient low emission alternatives to the conventional fuel-based automobiles. Depleting natural oil and fossil fuel reserves, rising petrol costs, and increasing governmental regulations to adopt more sustainable technologies have driven the development of plug-in electric vehicles.

The operation of PEVs and HPs in a distribution system will be a challenging demand side management problem from the utilities perspectives since PEV battery chargers and HPs represent sizeable loads. A quite plausible scenario is that numerous PEV owners will arrive home from work within a narrow time period and immediately plug-in their vehicles to charge during a time of already high peak demand. On the other hand, HPs work essentially during cold time period when Renewable Energy Sources (RES) are their lowest production. These uncoordinated and random charging activities could significantly stress the distribution system causing severe voltage fluctuations, sub- optimal generation dispatch, degraded system efficiency and economy, as well as increasing the likelihood of blackouts due to network overloads. Fortunately, the development of smart grid communication infrastructure will provide an excellent opportunity to manage this problem with intelligent or smart coordinated use of PEVs and HPs in the presence of RES [1].

The aim of this master thesis will be to assess the Demand Response potential of coordinated use of PEVs and/or HPS in the presence of RES in a block area. The MILP problem of an optimal scheduling will be solved using CPLEX and MATLAB. It will also include the stochastic behavior of the owners using probabilistic patterns that were previously developed.

 

[1]       S. Deilami, A. S. Masoum, P. S. Moses, and M. A. S. Masoum, “Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile,” IEEE Trans. Smart Grid, vol. 2, no. 3, pp. 456–467, Sep. 2011.

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