Performance assessment of three-phase PV tied NPC multilevel inverter based UPQC

Mohan, Harin M.; Dash, Santanu Kumar; Ram, Saswat Kumar; Caesarendra, Wahyu

doi:10.1109/iciccsp53532.2022.9862340

Cited by 8 publications

(1 citation statement)

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“…The efficient integration of EVs with microgrids will improve power system flexibility while lowering electricity costs. An intelligent microgrid energy management system incorporating EV charging will flatten the load profile, reduce peaks, and increase the use of DERs [20][21][22][23]. The authors of [24] proposed an AC microgrid with load demand-based control and onboard charging with diesel generators, but conversion and emissions are more significant.…”

Section: Literature Reviewmentioning

confidence: 99%

Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System

Mohan

Dash

2023

Systems

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Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested hybrid optimization approach aims to provide constant power regardless of the generation discrepancy and should prevent the early deterioration of the storage devices. This study suggests using a dynamic control system based on the Fuzzy-Sparrow Search Algorithm (SSA) to provide a reliable power balance for microgrid (MG) operation. The proposed DC microgrid integrating renewable energy sources (RES) and battery storage system (BSS) as sources are designed and evaluated, and the findings are further validated using MATLAB Simulink simulation. In comparing the hybrid SSA strategy with the most widely used Particle Swarm Optimization (PSO)-based power management, it was observed that the hybrid SSA approach was superior in terms of convergence speed and stability. The effectiveness of the given energy management system is evaluated using two distinct modes, the variation of solar irradiation and the variation of battery state of charge, ensuring the microgrid’s cost-effective operation. The enhanced response characteristics indicate that the Fuzzy-SSA can optimise power management of the DC microgrid, making better use of energy resources. These results show the relevance of algorithm configuration for cost-effective power management in DC microgrids, as it saves approximately 7.776% in electricity expenses over a year compared to PSO.

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