Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles

Soares, João; Sousa, Tiago; Morais, Hugo; Vale, Zita

http://hdl.handle.net/10400.22/5242

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Abstract(s)

The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs i n the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Othe r important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.

Keywords

Demand response electric vehicle Energy resource management Particle swarm optimization

URI

http://hdl.handle.net/10400.22/5242

Citation

Soares, J.; Morais, H.; Sousa, T.; Vale, Z.; Faria, P., "Day-Ahead Resource Scheduling Including Demand Response for Electric Vehicles," Smart Grid, IEEE Transactions on , vol.4, no.1, pp.596,605, March 2013 doi: 10.1109/TSG.2012.2235865