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The increasing use of renewable energy sources (RESs) in distribution networks, combined with an ever-increasing load, makes balancing between generation and load difficult. Demand response programs (DRPs) and energy storage come together in a significant way for microgrid operators to avoid purchasing power at a higher cost from the upstream network and to move toward a self-reliant system. This work considers Electric vehicles (EVs), being the most common type of flexible load, can be a demand response (DR) resource for demand-side management (DSM). The work proposes a technique that decides SoC over the number of EV plugged-in chargers in a charging station with time. To cope with the large number of chargers, the work conceptualizes cluster control in dealing with a large population of EV chargers. To achieve the goal, the control concept is implemented by categorizing EV chargers into different bins or groups based on the SoC of plugged-in EVs. The proposed model forecasts EV state transitions using the Markov state transition approach. The probabilistic approach of the state transition matrix is determined to understand the status of the battery SoC of EVs. Hence the significant contribution of the proposed technique does not necessitate sending the SoC values of plugged-in EVs to the control room at sampled intervals. The work then proposed two control techniques based on identifying and prioritizing the chargers of various charging stations to redistribute the EV loads within the short span of the load curve. Two control concepts, on/off control and charging power control, have been developed and applied to the final state transition matrix as a part of the DRP. The results related to the load reduction show the justification of the concept of controlling the power consumption of chargers. [ABSTRACT FROM AUTHOR]

In this work, a new optimisation technique called Mountain Gazelle Optimizer (MGO) has been applied to optimise the parameters of the proposed Fractional-Order Integrated Proportional Derivative with Filter (FOIPDF) controller. For a realistic analysis of load frequency control, a Multi-Area Multi-Source (MAMS) system that includes thermal, hydro, and gas-based plants has been taken into consideration. This system includes uncertainties and different constraints, such as the Governor Dead Band and Generation Rate Constraints. In addition, renewable energy sources (RESs) such as solar and wind power plants have been analysed to evaluate the impact of their fluctuating power supply on the grid’s frequency deviation. By evaluating the convergence curve, peak overshoot, peak undershoot, settling time, rise time, and integral of time multiplied squared error (ITSE), the FOIPDF controller tuned MGO technique demonstrates superior performance to alternative optimisation methods (Archimedes Optimisation Algorithm (AOA), Whale Optimisation Algorithm (WOA), and Grey Wolf Optimisation (GWO)). As a result, by optimising the FOIPDF controller using MGO, the ITSE value, which is a performance indicator, is decreased by 83.04%, 96.63%, and 41.79% when compared to the optimisation of the FOIPDF controller using AOA, WOA, and GWO, respectively. Furthermore, when considering a specific example, the utilisation of MGO leads to a notable enhancement in the time required for settling frequency variations in area-1, area-2, and tie-line power, with improvements of 18.26%, 8.73%, and 47.56% accordingly, compared to the outcomes achieved by the implementation of GWO. The proposed FOIPDF exhibited superior performance in normalising frequency deviation and tie line power when compared to other controllers such as Fractional Order PID (FOPID), Tilt Integral Derivative (TID), and PID. The results of the simulation suggest that the MGO-based FOIPDF controller significantly improves the frequency and tie-line power deviation of the system in the presence of various load disturbances, incorporation of renewable energy sources (RESs) such as wind and solar, sensitivity and stability analysis. Moreover, the effectiveness of the MGO-tuned controllers is verified by conducting tests in hardware in loop (HIL) mode using OPAL-RT. [ABSTRACT FROM AUTHOR]