Adaptive deep dynamic programming for integrated frequency control of multi-area multi-microgrid systems

Adaptive Dynamic Control Based Optimization of Renewable Energy Resources for Grid-Tied Microgrids

Renewable-energy-resource-based microgrids can overcome excessive carbon footprints and increase the overall economic profile of a country. However, the intermittent nature of renewables and load variation may cause various control problems which highly affect the power quality (frequency and voltages) of the overall system. This study aims to develop an adaptive technique for the optimization of renewable energy resources (RERs). The proposed grid-tied microgrid has been designed using a wind-turbine (WT) based distributed generation, a photovoltaic (PV) system, a diesel generator as an emergency backup, and battery energy storage system (BESS). The flexible (residential) and non-flexible (industrial) loads are connected with the proposed grid. Matlab/Simulink has been used to evaluate the performance of the proposed optimization technique. Comparison with different in-use techniques shows that the proposed technique is more reliable and efficient than the state of the art optimization techniques currently in use. Moreover, this proposed system provides robust optimization of parameters of concern such as frequency and voltages, makes efficient use of the maximum power point tracking while regulating voltages, reduces the overall system cost, and increases economic profitability.

A dynamic general type-2 fuzzy system with optimized secondary membership for online frequency regulation

This study suggests a new control system for frequency regulation in AC microgrids. Unlike to the most studies, challenging conditions such as variation of wind speed, multiple load disturbance, unknown dynamics and variable solar radiation are taken to account. To cope with uncertainties, a novel dynamic general type-2 (GT2) fuzzy logic system (FLS) by an optimized secondary membership is suggested. The secondary membership and rule parameters of dynamic GT2-FLS are online tuned through the adaptive optimization rules. The optimization rules are determined such that the robustness and stability to be guaranteed. Also, a new compensator is presented to tackle with estimation error and perturbations. The simulations verify that schemed controller outperforms than conventional methods.

Impact of Flexible AC Transmission System Devices on Automatic Generation Control with a Metaheuristic Based Fuzzy PID Controller

The present work proposes a teaching–learning-based optimization (TLBO)-tuned fuzzy proportional-integral-derivative (PID) controller of two-area hydro-thermal generating units for automatic generation control (AGC). The proposed system takes into account the physical constraints such as transport delay (TD), generation rate constraint (GRC), and governor dead band (GDB) nonlinearities. Firstly, fuzzy PID controllers were designed for both the areas and their gains were optimized using various minimization objective function criteria. Furthermore, applications of flexible alternating current transmission system (FACTS) devices such as static synchronous series compensator (SSSC), thyristor-controlled series capacitor (TCSC), thyristor-controlled phase shifter (TCPS), and unified power flow controller (UPFC) were investigated by integrating FACTS devices in appropriate locations of the system. The simulation results revealed that the minimum objective values were attained when the UPFC was placed in the system. Lastly, robustness analysis was done to observe the capability of the proposed controller with UPFC by changing system parameters and considering random load disturbances.