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Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles
Publication . Soares, João; Sousa, Tiago; Morais, Hugo; Vale, Zita
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.
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
5876-PPCDTI
Número da atribuição
PTDC/SEN-ENR/122174/2010