This paper presents a three‐area system tied together with tie lines. During the intervals of power mismatch, the system frequency deviates beyond the nominal value with an oscillatory response and the system may go to instability mode. The main purpose of the control strategy is optimizing the frequency fluctuations of the three areas and the deviations in the three tie line powers. This is achieved by three controllers as follows: the Tilt‐Integral‐Derivative (TID) controller, the Fractional Order Proportional‐Integral‐Derivative (FOPID), and the Proportional‐Integral‐Derivative (PID) controller. The three controllers are optimized by a new metaheuristic optimization algorithm based on the jellyfish behaviour in the ocean called the Jellyfish Search (JS) Optimizer. To prove the algorithm validity, it is compared with previous optimization techniques that have been applied to the study field such as Grey Wolf Optimization (GWO) algorithm and Genetic Algorithm (GA). Furthermore, renewable energy sources are implemented in the system such as wind energy and photovoltaic based on real data. Finally, energy storage devices (ESDs) like superconducting magnetic energy storage (SMES), capacitor energy storage (CES), and battery energy storage (BES) are implemented to improve the system behaviour due to the intermittent behaviour in the renewable sources.

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The constant changes in the load power lead permanently to a power mismatch between the power generation and the power consumption. So, the system frequency due to the power imbalance deviates from the nominal value. Consequently, a control loop should be implemented to stabilize the system frequency whenever a load change occurs. This paper presents a new super‐twisting sliding mode control methodology for obtaining an optimal frequency performance in a multi‐pool system. The paper presents a three‐pool system for frequency deviation problems using an optimal gain Super Twisting Sliding Mode Controller (STSMC), which regulates the frequency change and the line power change to zero in a minimal time. The extent of the excellence of the study proposed is evaluated by comparing it with three Benchmark classical controllers, which are the Tilt‐Integral‐Derivative (TID), Proportional‐Integral‐Derivative (PID), and Fractional‐Order PID (FOPID). The parameters of the four controllers are determined by a proposed physical meta‐heuristic optimization technique called Transient Search Optimizer (TSO), inspired by the dynamic behaviour in the electrical circuits comprising storage elements such as capacitors and inductors during the switching actions. The system simulation is performed, and the STSMC proved overwhelming superiority over other controllers, as it deals better with the transient interval of the system response. Renewable Energy sources (RESs) like photovoltaic and wind energy systems are established, and the STSMC is tested with industrial and residential load models. Finally, energy storage devices such as batteries and superconducting magnetic energy storage are implemented to suppress the rapid fluctuations in the system response, and it succeeded in doing that as the system oscillations are greatly damped with the energy storage devices.