Dynamic economic emission dispatch with load dema nd management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment

A multi-objective problem, namely, dynamic economic emission dispatch is simultaneously solved under several practical equality and inequality operating power constraints, while applying the load demand management (LDM) on 30,000 electric vehicles (EVs) during crest shaving and valley filling (CSVF) regions. A novel algorithm named orthogonal particle swarm optimization (OPSO) is proposed for such a problem. Ten thermal-generating units (TGUs) from power-generating systems (PGSs) in two Case Studies, with and without LDM on the load demand of 30,000 EVs, are tested. The comprehensive analysis results reveal that the quantity of emissions released by the 10 TGUs was remarkable affected and reduced around 4005 kg/day and the operating fuel cost was saved around $409/day when applying the LDM on the load demand of 30,000 EVs in the CSVF regions. This study provides important outcomes about the future operation of PGSs when applying the LDM strategy on a large-scale penetration of EVs in smart cities into a sustainable environment. The study outcomes contribute in making the PGS flexible, economical, environmental, stable, and reliable.