Enhancing transient response in AC microgrids: A multi-objective optimization approach for improved active power management

Many residential and local consumers have embraced single-phase inverters for self-consumption and energy trading. However, their adoption challenges the efficient management of electrical energy within microgrids (MGs), particularly regarding transient responses like rise time and overshoot, an appropriate active power control, or an optimum performance under different operating conditions. Conventional inverter controllers, while easy to program, often face conflicting objectives, where improving one parameter degrades another. This limitation complicates the control of nonlinear systems, risking high-energy transients that can damage components and reduce the lifespan of power semiconductors, leading to costly maintenance. This study proposes a robust strategy focused on primary control using a higher-order sliding mode controller (SMC) with a PI sliding surface tuned by multi-objective optimization (MOO) methods to address these issues. The control of active power is performed under the DQ frame synchronized to the main grid under a PLL method. Our approach aims to improve both the rise time and the overshoot of active power simultaneously. MOO techniques such as Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Differential Evolution (MODE), and Multi-Objective Adaptive Simulated Annealing (MOASA) have shown significant promise in our PSCAD-based research using data from an industrial inverter in an MG laboratory. The results were compared with particle swarm optimization (PSO) techniques. Performance indices like integral of absolute error (IAE), integral of square error (ISE), integral of time and absolute error (ITAE), and integral of time and square error (ITSE) demonstrated that SMC-2+MOO outperforms traditional methods like PSO, offering a superior solution for managing MG efficiency. • Development of a higher-order SMC algorithm for active power control, a technique rarely applied in microgrid systems. The proposed sliding surface adopts a PI-type structure, which is unconventional in standard SMC approaches. • Implementation of multi-objective optimization methods for controller tuning, addressing trade-offs between conflicting factors such as rise time and overshoot in active power generation within the DQ coordinate framework. • Experimental validation using a real microgrid setup to assess the proposed control strategy, offering a novel alternative to conventional microgrid systems where traditional control algorithms manage inverters. • Improvement of the inverter’s transient response by balancing competing parameters like rise time and overshoot, ultimately delivering technical and economic advantages for microgrid systems based on inverters. These benefits are particularly relevant to residential consumers, a rapidly expanding sector. • Evaluation of the multi-objective approach against a metaheuristic method, such as PSO, which is widely used in scientific studies aiming to optimize functions. In this way, it contrasts the optimization of a single objective, which is typically used in the majority of studies, with the optimization of many objectives at once, whose results are not as well explored due to their computational complexity. • Comparison of the results obtained between multi-objective optimization and PSO optimization from a qualitative point of view based on the proposed performance indices. In this regard, the performance of each control proposal is evaluated in a radar chart to show that the multi-objective proposal has advantages over other traditional optimization methods that have not been analyzed in depth in the current literature.