An Improved Multi-Objective Brain Storm Optimization Algorithm for Hybrid Microgrid Dispatch

An Improved Multi-Objective Brain Storm Optimization Algorithm for Hybrid Microgrid Dispatch

Kai Zhang, Zi Tang
Copyright: © 2024 |Pages: 21
DOI: 10.4018/IJSIR.336530
Article PDF Download
Open access articles are freely available for download

Abstract

The increasing integration of renewable energy sources into microgrids has led to challenges in achieving daily optimal scheduling for hybrid alternating current/direct current microgrids (HMGs). To solve the problem, this article presents a novel hybrid AC/DC microgrid scheduling method based on an improved brain storm optimization (BSO) algorithm. Firstly, with economic and energy storage device health as the primary objective functions, this paper proposes a dispatch model for AC-DC hybrid microgrids. To overcome the limitations of traditional algorithms, including premature convergence and can only find non-inferior solution sets, this article proposes a multi-objective BSO algorithm that integrates learning and selection strategies. Additionally, a fuzzy decision-making method is employed to achieve optimal daily dispatching and select the most suitable compromise solution. Finally, experiments are conducted to verify the effectiveness of the proposed multi-objective optimal scheduling method and to demonstrate the practicality and effectiveness of the method in real application scenarios.
Article Preview
Top

Introduction

With the increasing demand for electricity, the structure of the power grid is becoming more and more complex, with centralized power generation, large grids for long-distance transmission and other traditional grid structures also facing more challenges, including high costs, difficulty in operation, and difficulty in meeting the user's requirements for high quality, high reliability power and diversified power supply needs. In order to make the power grid run more securely and economically, the smart grid with distributed power supply as the main unit came into being. However, when a power system failure occurs, distributed power supply must immediately withdraw from operation, which limits the role of distributed power supply.

In order to enhance the benefits of distributed power sources, microgrids have emerged. A microgrid is a small-scale power generation and distribution system consisting of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, and the like. Microgrids effectively reduce the impact of photovoltaic (PV) and wind power output uncertainties on the traditional power grid (Das et al., 2022). To enhance microgrid development and leverage the advantages of AC and DC microgrids in managing renewable energy output uncertainties, the concept of AC/DC HMGs has emerged. These hybrids combine the strengths of AC and DC microgrids, offering a flexible energy management approach (Faraji et al., 2022; Zand et al., 2020; Zhang et al., 2018). They can accept both AC and DC energy sources, converting and controlling them through power electronic devices to accommodate various energy inputs (Ansari et al., 2020). As a result, AC and DC renewable energy sources can be seamlessly integrated into the AC/DC HMGs (Heidari et al., 2022; Jayaram et al., 2022).

The increasing penetration of renewable energy sources in AC/DC HMGs poses challenges for energy management (Ansari et al., 2020). Distributed energy storage systems play a vital role in addressing these challenges and find applications in different aspects of AC/DC HMGs (Elgamal et al., 2022). At the micro-power level, these systems can be efficiently scheduled and managed. However, micro-power sources, like photovoltaic generator sets and wind turbines, often exhibit fluctuating power output. Energy storage systems help balance energy supply and demand by storing excess energy for later use or releasing energy when needed to meet load demands. At the electric load level, distributed energy storage systems can be dispatched and managed as well. These systems store power during periods of low load demand and high renewable energy output, releasing power when load demand is high but renewable energy output is low (Gunantara, 2018). This scheduling balances the difference between grid load and supply, reduces pressure on the conventional power system, and enhances overall stability (Marler & Arora, 2004).

Complete Article List

Search this Journal:
Reset
Volume 15: 1 Issue (2024)
Volume 14: 3 Issues (2023)
Volume 13: 4 Issues (2022)
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing