This paper introduces a hybrid approach for power flow management (PFM) of the hybrid renewable energy source (HRES) connected microgrid (MG) system. Here, the proposed approach is the combination of both the squirrel search algorithm (SSA) with whale optimization algorithm (WOA) named as SSAWO. Here, the SSA is developing the control signals of the voltage source inverter subject to the difference of power exchange between the source side and load side. The multi-objective function is shaped by the grid required active and reactive power varieties generated based on the accessible source power. The WOA procedures guarantee to locate the online control signals by utilizing the parallel execution against the active and reactive power varieties. The proposed technique-based control model enhances the control parameters of the power controller in light of the power flow varieties. By using the proposed methodology, the PFM of the MG system is controlled based on the source side and load side parameters variations. In order to render the power demand by the grid, the present system is accountable for dominating the energy sources using, optimally, both renewable energy sources and energy storage devices. Finally, the proposed model is implemented in MATLAB/Simulink working platform and the execution equates with the existing techniques.
This manuscript proposes a hybrid method for system modeling and optimal allocation of low cost micro‐grid. The proposed hybrid method is the joint execution of the enhanced elephant herding optimization algorithm (EHOA) and adaptive neuro fuzzy inference system (ANFIS) named as EHO‐ANFIS. By using MG inputs, such as solar photovoltaic, wind turbine (WT), micro turbine (MT), fuel cell (FC), and battery energy storage system. EHO optimizes micro‐grid configuration in minimal fuel costs based on needed load requirement. Here, learning phase of ANFIS is utilized for predicting the load requirement. EHOA reduces operation and maintenance costs, emission cost on the basis of the predicted load requirement. The proposed method is executed in MATLAB/Simulink site and the robustness of the proposed method is compared with different existing methods. In the proposed method, the maximal generated power of photovoltaic represents 6 kW, wind turbine indicates 7.8 kW, micro turbine denotes 11.8 kW, FC implies 6.8 kW, and battery refers 3 kW. By utilizing genetic algorithm, the generated power of photovoltaic signifies 7 kW, WT implicates 6 kW, MT implicates 4 kW, FC refers 7 kW, and battery implies 14 kW. The proposed method has minimal cost effective depending on its load demand. The computational time of the proposed technique under 100, 250, 500, and 1000 trails is 5600, 14 000, 28 000, and 56 000 s.
-In the paper, a hybrid technique is proposed for detecting the location and capacity of distributed generation (DG) sources like wind and photovoltaic (PV) in power system. The novelty of the proposed method is the combined performance of both the Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) techniques. The mentioned techniques are the optimization techniques, which are used for optimizing the optimum location and capacity of the DG sources for radial distribution network. Initially, the Artificial Neural Network (ANN) is applied to obtain the available capacity of DG sources like wind and PV for 24 hours. The BBO algorithm requires radial distribution network voltage, real and power loss for determining the optimum location and capacity of the DG. Here, the BBO input parameters are classified into sub parameters and allowed as the PSO algorithm optimization process. The PSO synthesis the problem and develops the sub solution with the help of sub parameters. The BBO migration and mutation process is applied for the sub solution of PSO for identifying the optimum location and capacity of DG. For the analysis of the proposed method, the test case is considered. The IEEE standard bench mark 33 bus system is utilized for analyzing the effectiveness of the proposed method. Then the proposed technique is implemented in the MATLAB/simulink platform and the effectiveness is analyzed by comparing it with the BBO and PSO techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem
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