4.6 Article

A Novel Battery Supported Energy Management System for the Effective Handling of Feeble Power in Hybrid Microgrid Environment

期刊

IEEE ACCESS
卷 8, 期 -, 页码 217391-217415

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3039403

关键词

Microgrids; Batteries; Power distribution; Renewable energy sources; Hybrid power systems; Storage management; Tariffs; Battery based energy management system; solar PV; hybrid microgrid; conversion loss reduction; energy saving

资金

  1. TEQIP from MHRD

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One of the crucial challenges in the present power distribution system is the conversion loss phenomenon. Modern microgrid integrates various converters for varieties of applications, such as distributed power generation interconnection, energy storage management system, grid integration, demand management, etc. The increased usages of power converters further worsen the existing situation. Any initiatives taken towards energy conservation go in vain due to the excessive conversion loss phenomenon in the present distribution schemes. In this regard, a novel microgrid energy management scheme is proposed and developed to reduce the conversion losses in the residential distribution system. It uses a new control algorithm that finds the strength of power available in the DC side before being transferred. The conversion process is invoked only if the power is adequate, and if found feeble, then the conversion process is withdrawn and stored in an auxiliary battery. Conversion of feeble power would result in high loss across the converters and transformers. In this scheme, the AC loads are supplied by the utility grid, and the DC loads are fed by a solar PV and an auxiliary battery bank. The power conversion is done only during unavoidable circumstances. A prototype hardware setup has been developed, and the objective of the proposed research task has been validated. Further, the proposed scheme would gain importance in reducing the cost of the electricity for a time-of-use tariff system by optimization. A genetic algorithm is proposed to optimize the energy management of the microgrid system.

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