Managing electric vehicles (EVs) charging activities during times when the electrical grid is congested is a challenging task. This work proposes a dynamic, real-time pricing mechanism to accurately allocate more fairness to the billing strategy of EVs charging. The proposed mechanism is based on the concept of the inverse-demand function to reflect instantaneous energy prices in accordance with the microgrids' real-time energy supply. It is composed of two pricing fractions; retail energy price that represents energy cost for non-EVs demand during peak hours, and congested energy price allocated solely for billing EVs charging events during congested timeslots. The proposed methodology is implemented in a hierarchal multi-agent architecture with an optimal energy management system that aims to provide a cost-efficient microgrid operation. The input to the optimization problem are stochastic models that represent day-ahead PV production forecast, EVs energy levels and connectivity times prediction models based on a discrete-time Markov chain. Additionally, a predictive model of daily load demand is also presented based on adaptive Artificial Neural Network (ANN). We develop these models based on historical data for Miami-Dade County, South Florida. Through numerical simulations, we attest that the proposed pricing mechanism achieves significant energy price reduction when compared with results from previous published pricing policies.

The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem.

Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels.