Optimal P-Q Control of Grid-Connected Inverters in a Microgrid Based on Adaptive Population Extremal Optimization

Abstract

The optimal P-Q control issue of the active and reactive power for a microgrid in the grid-connected mode has attracted increasing interests recently. In this paper, an optimal active and reactive power control is developed for a three-phase grid-connected inverter in a microgrid by using an adaptive population-based extremal optimization algorithm (APEO). Firstly, the optimal P-Q control issue of grid-connected inverters in a microgrid is formulated as a constrained optimization problem, where six parameters of three decoupled PI controllers are real-coded as the decision variables, and the integral time absolute error (ITAE) between the output and referenced active power and the ITAE between the output and referenced reactive power are weighted as the objective function. Then, an effective and efficient APEO algorithm with an adaptive mutation operation is proposed for solving this constrained optimization problem. The simulation and experiments for a 3 kW three-phase grid-connected inverter under both nominal and variable reference active power values have shown that the proposed APEO-based P-Q control method outperforms the traditional Z-N empirical method, the adaptive genetic algorithm-based, and particle swarm optimization-based P-Q control methods.

Optimal Power Control of Inverter-Based Distributed Generations in Grid-Connected Microgrid

“…In the literature, several PQ control techniques have been presented to control the injected powers of the DGs in the grid-connected microgrid [21][22][23][24][25][26]. In our previous work [21], a power controller was implemented to calculate the dq reference currents using the dq output voltages and reference real and reactive powers.…”

“…In grid-connected microgrid, utilizing maximum power point tracking (MPPT), a PQ control method was proposed to control the power of the solar photovoltaic and battery storage [25]. PQ control was used in the load-following mode and PV control was utilized in maximum power point tracking mode to control a solar photovoltaic in distribution systems [26]. Unfortunately, all the previous presented work suffers from the bad performance especially under dynamic loads and generation variations since they are relying on deeply empirical engineering rules for designing the multivariable parameters of the PQ controllers [26].…”

“…PQ control was used in the load-following mode and PV control was utilized in maximum power point tracking mode to control a solar photovoltaic in distribution systems [26]. Unfortunately, all the previous presented work suffers from the bad performance especially under dynamic loads and generation variations since they are relying on deeply empirical engineering rules for designing the multivariable parameters of the PQ controllers [26]. Designing an optimal PQ controller is essentially to overcome the aforementioned problems and to solve the constrained optimization problem [26].…”

“…Unfortunately, all the previous presented work suffers from the bad performance especially under dynamic loads and generation variations since they are relying on deeply empirical engineering rules for designing the multivariable parameters of the PQ controllers [26]. Designing an optimal PQ controller is essentially to overcome the aforementioned problems and to solve the constrained optimization problem [26]. Recently, optimal control using swarm algorithms and popular evolutionary has been effectively utilized in power systems and power converters [27][28][29][30][31][32].…”

“…Recently, optimal control using swarm algorithms and popular evolutionary has been effectively utilized in power systems and power converters [27][28][29][30][31][32]. Moreover, designing an optimal PQ controller has been reported in a few works [26,30]. In [26], an optimal active and reactive power control was developed for a three-phase grid-connected inverter in a microgrid by using an adaptive population-based extremal optimization algorithm (APEO).…”