Real-time Locally Optimal Schedule for Electric Vehicle Load via Diversity-maximization NSGA-II

Abstract

As distributed energy storage equipments, electric vehicles (EVs) have great potential for applications in power systems. Meanwhile, reasonable optimization of the charging time of EVs can reduce the users'expense. Thus, the schedule of the EV load requires multi-objective optimization. A diversity-maximization non-dominated sorting genetic algorithm (DM-NSGA) -II is developed to perform multi-objective optimization by considering the power load profile, the users' charging cost, and battery degradation. Furthermore, a real-time locally optimal schedule is adopted by utilizing a flexible time scale. The case study illustrates that the proposed DM-NSGA-II can prevent being trapped in a relatively limited region so as to diversify the optimal results and provide trade-off solutions to decision makers. The simulation analysis shows that the variable time scale can continuously involve the present EVs in the realtime optimization rather than rely on the forecasting data. The schedule of the EV load is more practical without the loss of accuracy.

Optimisation of reverse salient rotor for interior permanent magnet synchronous motor and experimental validation

“…The Siemens dynamometer is used to balance the torque of the test motor and keep the rotor rotating at the constant speed. Furthermore, the torque of the test motor is controlled by the vector control strategy as presented in [30, 32]. The output torque of the test motor is measured by the torque sensor of HBM‐T40.…”

Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle Charging

“…V2G technology provides a service in which the electrical energy stored in an EV battery is transferred back to the power grid, which means that energy flows in both directions between the EV and the power grid. Reference [33] proposed a multi-objective integrated independent solution based on a dynamic pricing model to coordinate the V2G scheduling of EVs. A nonlinear autoregressive neural network cyclic load predictor is used for effective load fore-casting.…”

Planning integrated energy systems coupling V2G as a flexible storage

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