Electric vehicles (EVs) have many advantages over vehicles with an internal-combustion engine (ICE), and they have the capability to dominate the vehicles' market in the next years. In order to be charged, EVs require public charging stations that can be two-sided when vehicle-to-grid (V2G) services are indispensable to participate in an ancillary service market and in this case the capacity of the charging station should be estimated. Previous researches in the field of EVs used the queuing theory to model the arrival and departure times of EVs for estimating the V2G capacity. However, they did not consider the constraints of the queuing system. In this paper, a new queuing model is proposed to estimate the day-ahead capacity of a charging station, including balking, reneging, and retrial inside a finite-source queue system and phase-type services. A day-ahead service scheduling method is introduced for charging stations, which includes all constraints in the queuing model. Furthermore, control and management of the electric power at the charging station are carried out by using a nonpreemptive priority queue considering ac Level II and dc Level III chargers. Simulations are performed in three scenarios by using random input data of six types of EVs and the obtained results demonstrate that the proposed approach is able to obtain a realistic and precise estimation of the power capacity of a charging station.

This study performed a comparative analysis of five new meta-heuristic algorithms specifically adopted based on two general classifications; namely, nature-inspired, which includes artificial eco-system optimization (AEO), African vulture optimization algorithm (AVOA), gorilla troop optimization (GTO), and non-nature-inspired or based on mathematical and physics concepts, which includes gradient-based optimization (GBO) and Runge Kutta optimization (RUN) for optimal tuning of multi-machine power system stabilizers (PSSs). To achieve this aim, the algorithms were applied in the PSS design for a multi-machine smart power system. The PSS design was formulated as an optimization problem, and the eigenvalue-based objective function was adopted to improve the damping of electromechanical modes. The expressed objective function helped to determine the stabilizer parameters and enhanced the dynamic performance of the multi-machine power system. The performance of the algorithms in the PSS’s design was evaluated using the Western System Coordinating Council (WSCC) multi-machine power test system. The results obtained were compared with each other. When compared to nature-inspired algorithms (AEO, AVOA, and GTO), non-nature-inspired algorithms (GBO and RUN) reduced low-frequency oscillations faster by improving the damping of electromechanical modes and providing a better convergence ratio and statistical performance.

The significant ongoing technical and scientific advancements in renewable generation technologies and developments in smart grids' infrastructures have reduced their prices and costs to an affordable economical level. Hence, in the last decade, integration of renewable energies has had conspicuous pervasiveness, and the structures of power networks have been evolving to smart grid styles. The intelligently operated grids and more penetration of green energies facilitate alleviation of the generation expenditures, mitigation of greenhouse gases emissions, and achieving more efficient exploitation of installed generation resources. Aside from various uncertainties in power system operation, the inclusion of renewable resources, which are mainly intermittent in nature, encounters the power system operation scheduling with severe challenges. Therefore, the uncertainties of renewable resources such as wind and solar energy resources, as well as inherent uncertainties of power systems such as load forecast inaccuracies must be included mathematically in the operation schedules. The consideration of such uncertainties improves the robustness against plausible volatilities and contingencies and provides a more secure operation. The incorporation of various uncertainties into the unit commitment program deteriorates the solution of the problem in term of complexity. The solution of such a sophisticated problem which comprises time-oriented and practical constraints requires either appropriate exact or heuristic approaches. In this paper, a 10-generator test system is selected for simulations, and the virus colony search (VCS) algorithm is employed to solve unit commitment problem considering the impact of the presence of intermittent wind farms. Ultimately, the economic dispatch is performed between committed units, and operational costs are also calculated. The uncertainties of wind and load forecasts are incorporated in the system modeling through three scenarios. These scenarios demonstrate the impact of the presence of wind units in smart grids, and the way uncertainties affect the electricity network operation economically. Besides, the way wind farms must treat with such uncertainties through appropriate bidding strategy is investigated in order to protect themselves from high prices of spot market and plausible detriments.