Design and Control of Microgrid System for Optimum Operation - Jitender (My University, 2021)
A microgrid is equipped with variants of distributed energy resources offering a challenging situation to power engineers to maintain the power quality during its operation. So, monitoring and maintaining power quality is a critical issue for healthy operation of microgrid system up to consumer satisfaction. From the outcome of comprehensive literature review, it has been found that a decision-making methodology is required to assess the power quality of an AC microgrid through assessment of power quality-related electrical parameters such as voltage, frequency, power factor, total harmonic distortion (THD) etc. Hence, a decision making methodology is proposed to quantify the fuzziness in random variation of power quality. In this context, one of the major contributions of this research work is introduction of a power quality monitoring index (PQMI) which has been proposed using 256 rule-based fuzzy inference system (FIS) tested single-phase, as well as three-phase AC microgrid model. The results show the effectiveness of the proposed methodology to assess the status of PQMI, depending upon the acceptable limits of above mentioned electrical parameters, operating in grid-connected or islanded mode. The proposed methodology is verified through Mamdani and Sugeno type FIS using MATLAB-Simulink software. It is also found that the proposed PQMI is significant to define the status of microgrid even during transition from grid-connected to islanded mode and vice-versa. In this research work, it has been found that the battery energy storage (BES), intelligent protection and power quality are considered as independent issues, but these three factors can be interlinked effectively for control of microgrid through automated demand side management (ADSM). In this context, the proposed approach may change the conventional way for microgrid operation and control giving priority to the power quality issues considering protection and BES systems. The control algorithm is implemented through FIS based PQMI and ADSM. As a part of intelligent protection, the logic table for circuit breaker coordination has been developed without/with backup for relay devices to provide proper demand side management. The proposed scheme has been simulated using MATLAB-software and validated through experimental setup using OPAL-RT OP4510 real-time simulator for the assessment of power quality related parameters such as voltage, frequency, THD and power factor. In the present scenario of microgrid system, conversion of electrical energy has initiated a challenge to maintain the power quality within its satisfactory range which can be influenced by the voltage deviation, sag/swell, unbalancing, frequency, THD and power factor as per nature and location of loads and distributed energy resources (DERs). The existing literature show that the above mentioned parameters are not considered simultaneously for the assessment and controlling of power quality in PV based AC microgrid. To minimize the effect of these variables, a novel artificial neural network (ANN) based control approach has been proposed which can control the power quality as per IEEE/IEC standards. The proposed method has shown fast, smooth and stable operation while the performance of the same is verified with that of the proportional-integral (PI) and fuzzy-PI controllers in three-phase AC microgrid using MATLAB-Simulink software. Moreover, a small size microgrid model is tested for the assessment of power quality parameters considering the effect of line impedance and communication delay. Also, this model has been extended to a large size realistic microgrid structure for the feasibility study of proposed control methodology based on power quality related parameters. Simulation results of the realistic microgrid structure are found satisfactory considering the effects of line impedance, communication delay, demand response and off-nominal conditions during on-grid and off-grid operations.
Keywords: Microgrid; Operation and Control; Power Quality; Power Quality Monitoring Index (PQMI); Fuzzy Inference System; Artificial Neural Network