A Neuro-Fuzzy Controlled Solar Photovoltaic and PHEV based Frequency Regulation in a Microgrid without Storage System

Electric Vehicles (EVs) are rapidly replacing conventional fuel vehicles, offering powerful, emission-free performance. This paper introduces an innovative three-phase bidirectional charger for Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) applications, strengthening the connection between EVs and the power grid. The charger employs a two-stage power conversion approach with advanced converters and a simplified dq-based charging control strategy. An efficient AC-DC converter facilitates smooth transitions between modes, responding to grid directives for active and reactive power. A Soft Switching Dual Active Bridge (SS-DAB) DC-DC converter optimally interfaces with the EV battery pack, while dual active LCL filters suppress harmonics, enhancing system performance. Simulated results confirm the charger’s effectiveness in a 3.5kW prototype using MATLAB/Simulink.

Power quality is a growing concern with sensible and critical loads asking for stringent frequency regulations. The distributed generation sources like Solar Photovoltaic, Diesel Generator, Fuel Cells, and Battery driven Electric Vehicles (BEV) are considered as power sources in the system. For the power-sharing among these generators, a central controller with a novel simple adaptive-additive control strategy is used. Each source has a local controller to regulate the power outputs. This paper presents a Neuro-Fuzzy controller for operating Solar Photovoltaic power at a Limited Power Point while a novel Neuro-Tuned-Fuzzy Controller decides which BEVs to be connected to the microgrid by giving a priority value to each BEV. The simulation results show the coordinated operation of the central and local controllers to regulate the power outputs of the sources was effective to manage reserves and achieve frequency regulation.

There is a growing concern to be self-sufficient and reduce the dependency on external power generating sources to satisfy the energy demands. Issues such as integrated operation of these power sources without having a dedicated storage system with enhanced frequency regulation is to be addressed. In this paper, the distributed generating sources like solar photovoltaic, diesel generator, fuel cells and electric vehicles are considered. Electric vehicle participation in the frequency regulation comes with constraints on state of charge of the battery and availability of the vehicle. An adaptive-additive algorithm is proposed for performing energy resource management and to maintain the frequency within the allowable band during transient and steady state system conditions. Load variation and EV-fleet availability variations are considered in the paper for understanding the system’s response to frequency changes by performing small-signal-analysis. The results show coordinated and satisfactory response of the system to maintain frequency regulation. Economic viability is also focused in the paper.

Cascaded multi-level inverters are gaining much attention by researchers in recent times due to its easy implementation and control. A better approach to reduce total harmonic distortion (THD) in all the three L-L voltages of three-phase multi-level inverters is presented in this paper. It is a fundamental frequency switching strategy where the switching angles can be so selected such that THD in output L-L voltages is minimized to a great extent. In order to obtain most favorable switching angles, an optimisation algorithm using GA is applied to L-L output voltages of three phase multi-level inverters. From three-phase load view point, THD in L-L output voltage of three phase multi-level inverters is a major apprehension when compared to phase voltage. Keeping this in view, optimisation algorithm using GA is applied to L-L voltages (RY, YB, BR) of all the three phases of cascaded multi-level inverters for minimisation of THD. The number of levels in the output will be dynamic and is based on the switching angles chosen and accordingly variations in THD levels too. To support simulation results, hardware for producing triggering pulses of the switches are realized, with the switching times determined by the genetic algorithm and generated using STM32F04 DSP passing through a driver (IR2110) cabinet.