Enhancing Load Frequency Control of Renewable Energy based Hybrid Microgrid Integrated with EV Aggregator using Transit Search Optimization

High penetration of RES interconnection with power electronics inverters-converters has led to decreased system inertia which increased frequency fluctuation in power system. This article considers a hybrid microgrid consisting of various type of RESs such as solar, wind which are very irregular in behavior. To compensate the frequency oscillation due to load disturbance and variation in RES, capacitive energy storage (CES) is incorporated in this paper as CES is a fast acting energy storage system. Variation of RES parameters and multi-load fluctuation leads to the frequency deviation and imbalance of demand and generation. To compensate the frequency deviation and to balance the demand and generation a fractional order PI plus one (FOPI+1) controller is proposed. Electric vehicle (EV) aggregators are also used to compensate the real power imbalance. Multiple nonlinearities such as generation rate constraints, governor dead band are taken into consideration. The proposed controller parameters are tuned by artificial intelligence (AI) based a nature-inspired metaheuristic optimization algorithm namely Transit Search Optimization Algorithm (TSO). It is essential to employ an advanced, rapid and accurate AI based technique as the application of AI helps to improve the reliability, stability, security and efficiency of microgrid. Sensitivity of the proposed controller is investigated at different scenarios considering load perturbations, wide variation of system parameters as well as solar and wind power fluctuations.