Large frequency oscillations happen when the unbalanced power is not compensated by Load Frequency Controller (LFC) system. In recent years, the using of electric vehicles (EVs) has increased with renewable generation sources such as solar cells, wind turbines, fuel cells, and so on. The large-scale integration of these new types of generation sources and demand in power grids will have significant impact on operations, planning and stability control. This work sets out to design an effective PID controller for LFC in an island Micro-grid (MG) and also proposes a model for EVs to contribute to LFC system. To this end, particle swarm optimization (PSO) based artificial neural network (ANN) technique is considered to tune the parameters of PID controller in MG structure. Simulation results demonstrate that with PSO-based ANN, the system is becoming stable in shortest time. In addition, frequency oscillation magnitude, overshoot and settling time are reduced.

Electric vehicles (EVs) battery has the ability to enhance the balance between the load demand and power generation units. Random behaviour of owners and EVs low capacity are some factors that develop the EV aggregator notion to increase the EV participation in the ancillary services market. In the presence of EVs aggregator, the frequency control service is capable of causing time‐varying delay in load frequency control (LFC) systems. Owing to the dependency of controller effectiveness on its parameters, these parameters should be designed optimally in order to have a better result in an LFC system in the presence of time‐varying delay. Therefore, a salp swarm algorithm (SSA) is utilised to adjust the fractional‐order proportional integral derivative (PID) (FOPID) controller coefficients. Also, some evaluations are performed about the proposed LFC performance by an integral absolute error, integral time absolute error and mean absolute error indicators. In this study, both single and two‐area LFC systems containing EVs aggregator with time‐varying delay are simulated and analysed. The results indicate that the proposed controller has fewer frequency variations in contrast to other controllers presented in the case studies. Moreover, it is concluded that the FOPID controller could significantly decrease the overshoot and settling time of the frequency variation signal.

This article deals with the stochastic scheduling of a microgrid (MG) to balance the economical and resilience metrics. In the proposed model, the MG resilience indices are integrated into the economic criteria to ensure the resilience of MG operation alongside the main MG actors' profit/loss. The MG fragility index, recovery efficiency index, MG voltage index, and lost load index are considered in the proposed planning model. Further, to make the results more realistic, the uncertainties associated with energy price and wind production, alongside with planning of energy storage systems and electric vehicles parking lots are considered. To achieve a better solution for the security-constraint operation of MG, AC network equations are included in the system modeling. Finally, a large-scale MG based on the IEEE-33 bus testbed is utilized to evaluate the effectiveness of the proposed stochastic scheduling program.