Evolutionary Algorithms Based Load Sensitive Dynamic EMS for Hybrid Renewable Energy System

Abstract

Renewable sources of energy have become an essential part of meeting the demands newlinefor energy resources to satisfy the demand for energy production, along with providing no newlineecological change and contribute to meaningful development. But Renewable Energy newlinesources are irregular in nature due to weather conditions, which in turn hinders stable newlinepower supply. Combining different Renewable Energy sources can be a solution not only for newlinethe reliable power supply but also for a reduction in cost or economic Factors and storage newlinecapacity. Design is technically driven with a foot of economic analysis regarding return on newlineInvestment and Payback Period. Advanced Optimization algorithms and HOMER energy newlineSoftware Tools used for the design of HRES. Along with the Optimization in HOMER, newlinemathematical modeling is also done for HRES. From the Optimization results; it is observed newlinethat PV and wind are excellent hybrid energy sources. newlineComplex issues like non-linear power generation patterns as well as dynamic newlineload variations are settled by various progress techniques with different Evolutionary newlinealgorithm approaches. Evolutionary algorithms like the Genetic Algorithm, Flower newlinePollination Algorithm, Particle Swarm Optimization, and Hyper Spherical Search along with newlineClassical PID Controller, Fractional-order PID Controller, and ITAE as the objective newlinefunction are used to meet the goal of tuning PID and FOPID Parameters. Further, newlineOptimization Evolutionary algorithms will try to reduce the objective function value of ITAE newlineto meet the reliability of power supply. The Comparison is made among Four Evolutionary newlinealgorithms with both PID Controller and Fractional Order PID Controller considering newlinedifferent Parameters like rising Time, Settling Time, Objective Function Value, Overshoot, newlineand Peak time. Finally, considering the minimum value of different parameters the best newlineEvolutionary algorithm is evaluated. newlineRecent Trend of Evolutionary Algorithms has better accuracy, good convergence in newlinecomparison to traditional methods. The entire S