The proposed work addresses the modeling, control, energy management and operation of hybrid grid connected system with wind-PV-Battery Energy Storage System (BESS) integrated with Fuel Cell (FC) and Electrolyzer. A hybrid PV-Wind-FC with electrolyzer consisting of BESS with the least number of control loops and converters has been proposed. The proposed hybrid system presents a cost efficient solution for integrating PV into a hybrid system by eliminating the PV converter. This includes the design of controllers for grid-connected hybrid systems with a renewable distributed generator (Wind and PV) as a primary source, BESS as a secondary source and FC with Electrolyzer as a tertiary source. In addition, the lead compensator along with integrator is used for obtaining enough phase margin and removing steady state error completely. It increases the stability of the controller and adds phase shift $\varphi _{s}$ at a cross gain frequency ( $\omega _{cut}$ ). The Grid Side Controller (GSC) is capable of providing frequency support to the utility grid, when it is linked to the grid. In the proposed configuration, PV power is maximized and injected into grid through GSC. Rotor Side Converter (RSC) and GSC ensure the support for sharing the burden of the grid station. Moreover, the proposed controller of BESS with coordination of FC eliminates the effect of intermittency of power generated from wind and PV. Excess power production by renewable distribution generation is used by Electrolyzer to generate hydrogen. This hydrogen is further used by FC when there is not enough power generation due to unfavorable weather conditions. The energy management has been presented to fulfill the load profile, avoid BESS overcharging and to minimize the intermittency and fluctuation of Wind and PV sources. This method guarantees steady power flow and service continuity. The Simulink model of the proposed system results validate the efficiency of the proposed hybrid system as compared to the conventional hybrid system reported in the literature. The modeling of the proposed system and analysis has been demonstrated using the MATLAB Simulink model. Lastly, the energy management of the system has also been examined and compared with the conventional power system.

The rapid utilization of electricity has forced the stability of the power system for continuous operation. Due to difference in demand and generation, the frequency reference point changes that needs to be restored to its locus point for the stable operation of the power system. Therefore, a novel efficient control design is propounded to counter the phenomena of load frequency control. The designing of cascaded fractional model predictive controller coupled with fractional-order PID controller (CFMPC-FOPID) is designed for an efficient response of the power system under load disruption and system parameter variations. The controller is optimized by sooty tern optimization algorithm to identify the optimal parameters of the controller. The controller is tested under power mixing of renewable energy sources (i.e., PV and wind) and under varying load scenarios in multiarea hybrid power system. The proposed controller has effectively handled the frequency disruption under distinct load change by stabilizing it in 1.34 sec, 0.60 sec, and 0.41 sec for area-1 with an average time of 0.78 sec, and for area-2, the stabilizing time is 1.40 sec, 0.89 sec, and 0.56 sec with an average time of 0.95 sec, whereas the average time for MPC/PI, DSA-FOPID, GWO: PI-PD, and SCA: FOPI-FOPID is 7.67 sec, 4.68 sec, 1.77 sec, and 4.72 sec, respectively, for area-1 and 6.47 sec, 5.13 sec, 3.45 sec, and 5.02 sec, respectively, for area-2. The outcome result justifies the superiority of the studied technique.

Photovoltaic (P.V.) systems have become an emerging field for power generation by using renewable energy (RE) sources to overcome the usage of conventional combustible fuels and the massive release of dangerous gases. The efficient operation of the PV system is vital to extracting the maximum power from the PV source. For this, a maximum power point tracking (MPPT) algorithm works with a DC–DC converter to extract maximum power from the P.V. system. Two main issues may arise with the involvement of a converter: (1) to locate M.P.P and (2) the performance of the PV model in varying weather conditions. Therefore, designing any converter gain has the utmost significance; thus, the proposed work is on non-isolated boost converters. To calculate the values of specific parameters such as input capacitor, output capacitor, and inductor, the averaging state-space modeling typically uses governing equations. In this research, the formula of the input capacitor is derived through the average state-space modeling of the boost converter, which signifies the relation between input and output capacitors. From the results, it has been proven that the input capacitor efficiently performs when the input capacitor is half of the output capacitor. At an irradiance level of 1000 W/m², the system shows stable behavior with a fast convergence speed of 0.00745 s until the irradiance falls to a value of 400 W/m². The system is less stable during the morning and the evening when irradiance falls are very low.

The technology has proceeded so much that the power system should be substantial and explicit to give optimal results. Ever-increasing complexities of the power system and load disparity cause frequency fluctuations leading to efficiency degradation of the power system. In order to give a suitable real power output, the system entails an extremely perceptive control technique. Consequently, an advanced control method, that is, an adaptive model predictive controller (AMPC), is suggested for load frequency control (LFC) of the series power system which comprises photovoltaic (PV), wind, and thermal power. The suggested method is considered to enhance the power system execution as well as to decrease the oscillations due to a discrepancy in the system parameters and load disturbance under a multi-area power system network. The AMPC design verifies the constant frequency by maintaining a minimum steady state error under varying load conditions. The proposed control approach pledge that the steady-state error of frequencies and interchange of tie line powers is maintained in a given tolerance constraint. The effectiveness of the proposed controller is scrutinized by conventional controllers like genetic algorithm-tuned PI (GA-PI), firefly algorithm-tuned PI (FA-PI), and model predictive controller (MPC) to show the competence of the proposed method.