Photovoltaic (PV) systems need maximum power point tracking (MPPT) algorithms to deal with the nonlinear current–voltage behavior of solar modules and the changing amount of sunlight. This paper shows how to model, simulate, and test the performance of a solar charge controller that uses the Incremental Conductance (IC) MPPT algorithm for standalone PV-battery systems. We create a full hybrid mathematical model that combines the PV diode model, the IC MPPT control law, the DC-DC buck-boost conversion, and the dynamics of charging a battery. By comparing incremental and instantaneous conductance terms, the IC method finds the maximum power point (MPP). It also tracks better when the irradiance changes. We use a variable irradiance profile that ranges from 100 to 1000 W/m2 in MATLAB/Simulink simulations to mimic how the sun really works. The results reveal that the IC controller rapidly drives the PV voltage to its optimum operation point with less oscillation, making it capable of charging a 12 V battery from a 29 V PV module. The proposed system can efficiently harness energy from the environment for the purpose of charging batteries in a stable manner; thus, it is very suitable for decentralized off-grid renewable energy applications.

The growing popularity of multi-microgrids (MMGs) is driven by their enhanced reliability and resilience. However, efficient protection of MMGs is challenging due to changes in topology and fluctuations in short-circuit current levels across different configurations. This paper proposes a setting group based protection scheme using directional overcurrent relays (DOCRs) to provide adequate protection for MMGs. A matrix of fault currents is generated for each possible topology of microgrids to divide them into eight cluster groups. The relay coordination problem is formulated as a nonlinear programming problem. The relay settings are optimally determined using the function minimizer with linear and nonlinear constraints (fmincon) and genetic algorithm (GA) to minimize the total relay operating time, considering different IEC standard relay characteristics curves. The effectiveness of the proposed protection approach has been verified using an IEEE benchmark test system for MMGs.

Traditional protection systems rely on overcurrent relays (OCRs), which are less adaptable to modern microgrids (MGs) with evolving dynamics caused by the increased integration of distributed energy resources (DERs). This paper proposes a modified fault-resistance-based relay characteristic to enhance the operation of directional overcurrent relays (DOCRs) under high-impedance faults (HIFs). The particle swarm optimization (PSO) algorithm is used to determine the optimal relay settings based on normal and fault conditions data from real-time simulations. The PSO also determines the optimal hyperparameter values for an optimal deep neural network (DNN) structure. The DNN model is trained to optimize relay settings across a wide range of fault resistance values while minimizing the number of required simulations. This framework incorporates a continuous learning mechanism to dynamically adapt relay settings in response to changing system conditions, and enhance fault sensitivity, relays’ coordination, and operating speed. It is demonstrated that the proposed method improves the reliability of AC MGs against grid faults by offering an enhanced coordinated protection solution.

Electric Vehicles (EVs) are gaining more and more popularity in today’s world due to their exceptional advantages. Effective planning is required to carry out EV charging smoothly, as the random charging process could lead to problems like transformer overloading and increased feeder energy losses. The proposed work includes the implementation of a G2V scheme for smart charging of vehicles with charging cost minimization and V2G technology into the distribution network to support grid voltage. An IEEE 33 bus distribution network is used as the test system for evaluating the proposed smart charging strategy. The main objective is to minimize the overall charging costs for EVs. It is done by developing an objective function that considers various cost factors associated with EV charging. Vehicle-to-grid (V2G) technology is incorporated into the system to allow EVs to discharge energy back to the grid when needed. This bi-directional energy flow supports grid voltage regulation and enhances the overall stability of the distribution network. Linear programming is employed to solve the objective function. This mathematical approach is chosen for its effectiveness in handling linear constraints and objective functions, ensuring an optimal solution for cost minimization.

Connecting electrical vehicle (EV) to the grid has faced many challenges. In this paper on board and off board charging schemes for EV is proposed based on voltage and state of charge (SOC). An adaptive controller is designed which resolve many problems faced by previous charging schemes. Controller compares the EV voltage at point of charging (POC) with the predefined reference voltage and reduces charging as POC voltage approaches this reference. Battery SOC and owner’s preferred end of charge time are taken into account for reduced charging rates. Extensive simulation results have been shown to validate the proposed controller. Controller performance checked by considering voltage control devices in distribution system to maintain the system within acceptable limits. Bidirectional controlling has also been tested.V2G, V2V schemes are also implemented using bidirectional converter.