This paper proposes a cascaded Proportional Integral-Fractional Order Proportional-Integral-Derivative (PI-FOPID) controller to improve the frequency response of a hybrid microgrid system. The optimum gains of the proposed controller are fine-tuned using Gorilla Troops Optimizer (GTO) which is a recent metaheuristic optimization algorithm. The case study is a two-area microgrid system that contains diesel generators, various renewable energy sources such as photovoltaic and wind generation systems, as well as different energy storage devices. Moreover, real wind speed and solar irradiance measurements have been collected for proper system modeling. The performance of the proposed cascaded PI-FOPID controller is compared to the single structure fractional order PID (FOPID) controller based on GTO and numerous other optimization techniques presented in the previous literature such as Genetic Algorithm and Particle Swarm Optimization. The robustness of the proposed cascaded PI-FOPID controller is investigated under different scenarios such as different step load perturbations, random load disturbances, and renewable energy sources variation. The simulation results are carried out using MATLAB/Simulink. The results show that the proposed controller provides an improvement in the maximum overshoot/undershoot and settling time of 99.8% and 75.9%, respectively, compared to other competing techniques.

The concepts of microgrids (MGs) and smart grid represent the recent targeted revolution towards fully smart electrical network integrated with high penetration of renewable energy sources (RESs). The protection system of MGs becomes a challenge due to variable characteristics of its currents, bidirectional power flow and output power fluctuations of RES, causing selectivity and sensitivity issues for conventional protective devices (PDs) with fixed setting. In this paper, a smart protection scheme (SPS) is proposed using micro-phasor measurement units (µPMUs) to obtain the continuous rapid synchronized phasor measurement data. And it is communicated with a microgrid central controller (MGCC) through highly reliable communication architecture to carry out online smart adaptive protection scheme. Fault index coefficients and abnormality coefficients are calculated for each feeder to detect the fault location and the abnormality case. Detailed modeling of an MG including 10-bus connected distribution system with integrated distributed generation (DG) is simulated using ETAP software. The proposed protection algorithm is modeled and evaluated using MATLAB software. The proposed fault detector and abnormality detector can enable quick and accurate fault identification and isolation.

The integration of photovoltaic and battery energy storage systems into utility grids is favorable for electricity customers, especially for high consumption load patterns due to the high electricity bill. To increase the annual bill savings and decrease the dependency on the utility grid, a procedure of optimally sizing the PV battery system is presented in this paper. A MATLAB-based code of the genetic algorithm is used to maximize the system self-sufficiency and minimize the discounted payback period while guaranteeing the system profitability. The optimization technique is introduced under the time-of-use tariff structure for residential prosumers in two scenarios. The first introduces a sellback energy price equal to the off-peak energy price, and the second introduces a sellback energy price equal to the on-peak energy price. The economic and technical effects of this optimization are described and analyzed. The effect of PV system capital cost, battery capital cost, and discount rate variations is evaluated by sensitivity analysis. The results indicate that for a sellback price equal to the off-peak price, a minimum discounted payback period can be achieved by installing the maximum PV system size without batteries, and it is reduced to half of its value for a sellback price equal to the on-peak price at the same PV system capacity. For a sellback price equal to the off-peak price, the minimum DPBP of 5.8 years can be achieved by installing a 20 kW _p PV system without batteries with 47% self-sufficiency and 2.4 years for a sellback price equal to the on-peak price by installing the same system capacity. The proposed approach highlights that increasing the battery size leads to high self-sufficiency without a considerable decrease in the annual bill savings that encourage customers to invest in the PV battery system.

The tendency towards carbon dioxide reduction greatly stimulates the popularity of electric vehicles against conventional vehicles. However, electric vehicle chargers represent a huge electric burden, which affects the performance and stability of the grid. Various optimization methodologies have been proposed in literature to enhance the performance of the distribution grids. However, existing techniques handle the raised issues from individual perspectives and/or with limited scopes. Therefore, this paper aims to develop a distributed controller-based coordination scheme in both medium and low voltage networks to handle the electric vehicles’ charging impact on the power grid. The scope of this work covers improving the network voltage profile, reducing the total active and reactive power, reducing the load fluctuations and total charging cost, while taking into consideration the random arrivals/departures of electric vehicles and the vehicle owners’ preferred charging time zones with vehicle-to-grid technology. Simulations are carried out to prove the successfulness of the proposed method in improving the performance of IEEE 31-bus 23 kV system with several 415 V residential feeders. Additionally, the proposed method is validated using Controller Hardware-in-the-Loop. The results show that the proposed method can significantly reduce the issues that appear in the electric power grid during charging with minor changes in the existing grid. The results prove the successful implementation of different types of charging, namely, ultra-fast, fast, moderate, normal and vehicle-to-grid charging with minimum charging cost to enhance the owner’s satisfaction level.