The penetration of renewable energy sources into the conventional power systems are evolving day by day. Therefore, in this paper, a photovoltaic (PV) connected thermal system is discussed and analyzed by keeping PV to operate at maximum power point (MPP). The main problem in the interconnection of these systems is load frequency fluctuations due to different load changing conditions. The model predictive controller (MPC) has the ability to predict the target value at real-time with fast convergence. Therefore, MPC is proposed to negate this problem by giving minimum oscillation. The comparison analysis is carried out with other conventional controllers, including genetic algorithm-based PI, firefly algorithm-based PI and PI controller. Simulation results clearly exhibit the outclass performance of MPC over all other controllers.

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Summary This paper introduces a novel genetic optimize multi‐control adaptive fractional order PID (AFOPID) for Photovoltaic (PV) and Wind connected grid system. The proposed AFOPID controller is optimized by a genetic algorithm (GA) to initialize the controller parameters. The renewable energy sources are mathematically modeled using multi control approach (MCA). In the proposed work, the MCA involves the maximum power point tracking (MPPT), dc link voltage, and current control and quadrature axis modeling. Furthermore, the current control functionalities are performed through an adaptive approach of AFOPID, where the controlling parameters are updated by measured error at every instant. The idea behind the research is to improve the tracking efficiency by introducing better control in order to gain maximum power from the source with minimized total harmonic distortion (THD). The proposed control scheme is tested using computer‐aided experimentation by varying the output of renewable energy sources, inverter uncertainty, and grid voltage variations. The results are benchmarked against the conventional fuzzy logic controllers, fractional‐order PID, and PI controllers. Moreover, to evaluate the effectiveness of the proposed controller, the MCA‐AFOPID is compared with ant colony optimization (ACO) and particle swarm optimization (PSO) optimized FOPID controller respectively. The proposed controller outperforms as compared to other controller.

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.

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 φ s at a cross gain frequency (ω 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.INDEX TERMS Grid-connected system, Energy management system, Battery energy storage system, Electrolyzer, Fuel Cell, Doubly fed induction motor, Maximum power point tracking I. INTRODUCTIONAn increase in number of home appliances to do simple tasks of the household has made life very relaxed and effortless.Whereas with the inclusion of each electric appliance at home the electric demand is growing. Power generation is continuously increasing to fulfill the electric power demand.