A Novel Approach to Multi-Objective Control for Battery-Enabled Hybrid Micro Grid

Abstract

In this study, we present a new technique for overcoming the operational difficulties faced by Microgrid (MG) systems, which are capable of functioning in both islanding and grid-connected states. As the expenses and requirements of traditional energy sources rise, there\'s been a growing focus on renewable energy alternatives. A key issue in the operation of MG systems is the efficient management of control to ensure the highest possible power output despite the variability in generation. To tackle this, we suggest a power-sharing strategy that is optimized for the components of Solar Photovoltaic (PV), wind, and Battery Energy Storage Systems (BESS). Our approach is environmentally sustainable, reducing pollution and greenhouse gas emissions, which in turn benefits the natural environment. We formulate a multi-faceted objective function designed to enhance system efficiency while keeping costs to a minimum. By employing the Genetic Wolf Optimization (GWO) algorithm, we achieve outstanding outcomes in lowering the financial expenses associated with microgrid electricity production, surpassing traditional optimization techniques such as Particle Swarm Optimization (PSO) and Bacteria Foraging Optimization (BFO). Furthermore, we introduce a control framework that organizes the operation of each subsystem to ensure the stability of frequency in the face of unpredictable generation and demand. A comparative study validates the effectiveness of our approach, highlighting the GWO algorithm\'s advantage in optimizing microgrid performance.

Introduction

I. INTRODUCTION

The surging demand for electricity has accelerated the adoption of alternative energy sources. While traditional power generation systems can operate independently or in conjunction with the main grid, their increasing costs and environmental concerns have driven the exploration of Distributed Generation (DG). Unlike centralized power plants, DG offers localized power production tailored to specific needs. These systems can operate autonomously or be integrated into the grid, facilitating the creation of microgrids (MGs) to reduce grid size.However, Renewable Energy-Based Systems (REBS), the cornerstone of many DG systems, face challenges due to their intermittent nature and the complexities of managing multiple DG units. Recent advancements in power electronics have enabled the seamless integration of various REBS into both standalone and grid-connected configurations. To ensure reliable and stable power supply, microgrids incorporate sophisticated control strategies. Countries with abundant solar resources, such as India, often combine solar and wind power to mitigate the challenges of intermittent generation. The use of droop control in microgrids is particularly recognized for its reliability and [missing information].

Solar power with wind energy systems to ensure a continuous power supply. The use of droop control in Microgrids (MGs) is commended for its reliability and the low need for communication, making it easy to add new units. However, the traditional droop control techniques, which take into account line impedance, voltage, and frequency, still struggle with stability issues. This paper introduces a new hierarchical droop control method aimed at ensuring power delivery and maintaining operational frequency across all Distributed Generation (DG) units, even in the face of changing demand. Each DG unit will use the Penguin Search Algorithm (PSA), a meta-heuristic optimization technique inspired by the cooperative hunting tactics of penguins. The PSA algorithm has shown superior performance compared to other methods like Particle Swarm Optimization (PSO) and Bacteria Foraging Optimization (BFO). The scheme is implemented in MATLAB and tested under various load conditions. The results demonstrate that the proposed hierarchical supervisory control is effective in delivering power, maintaining frequency, and achieving performance standards under different scenarios. This approach also supports economic goals, reduces carbon emissions, and minimizes the use of batteries, thereby extending battery life and reducing the overall cost of the system.

VII. ACKNOWLEDGMENT

The authors declare that every piece of information provided is totally our own creation and has not been imitated from any other source

Conclusion

This research presents a structured control system framework that effectively supplies power to the utility even with fluctuating Distributed Generation (DG) and demand patterns. Moreover, a strategy that balances multiple goals ensures the system operates with the lowest possible maintenance expenses. By setting up charging and discharging schedules for Battery Energy Storage Systems (BESS), the risk of system failures during periods of reduced DG production is reduced. The results underscore the effective functioning of Microgrid Control operations. Future studies could investigate the integration of variable energy sources and non-linear loads to improve the system\'s resilience and performance.

References

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Copyright

Copyright © 2024 Narasimhulu ., Azka Ihtesham Uddin Ahmed, Dr. P. Venkata Prasad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.