Scholarlink

<p>The efficient utilization of wind energy, photovoltaic energy, and other renewable resources can significantly reduce costs for the power system while mitigating pollution. Optimizing distributed energy source scheduling in microgrids is a complex challenge. This paper presents a multi-strategy fusion Harris Hawks optimization algorithm (MSHHO), aimed at addressing the economic scheduling problem in microgrids. MSHHO integrates several innovative strategies, a piecewise compound one-dimensional chaotic map for population initialization, an adaptive double cosine shrinkage strategy to regulate escape energy variation, and a Pad<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10586_2024_4685_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="11" /> </InlineMediaObject> <EquationSource Format="TEX">\(\acute{e}\)</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>e</mi> <mo>´</mo> </mover> </math></EquationSource> </InlineEquation> approximation method to enhance search precision. Furthermore, MSHHO integrates a specialized random agent to ensure progressive search advancement. This paper compares the performance of MSHHO with other advanced algorithms using 23 benchmark functions and 8 engineering problems, providing a rigorous evaluation of their performance and rankings. In the economic scheduling problem of microgrid, MSHHO achieves an impressive reduction in operating costs by 16.31<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10586_2024_4685_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation>, demonstrating its superiority.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hybrid strategy improved Harris Hawks optimization algorithm for global optimization and microgrid economic scheduling problem

  • 期刊论文

  • 发表日期：

    2025年1月21日

查看全文
  • Tianbao Liu,
  • Yue Li,
  • Xiwen Qin

摘要

The efficient utilization of wind energy, photovoltaic energy, and other renewable resources can significantly reduce costs for the power system while mitigating pollution. Optimizing distributed energy source scheduling in microgrids is a complex challenge. This paper presents a multi-strategy fusion Harris Hawks optimization algorithm (MSHHO), aimed at addressing the economic scheduling problem in microgrids. MSHHO integrates several innovative strategies, a piecewise compound one-dimensional chaotic map for population initialization, an adaptive double cosine shrinkage strategy to regulate escape energy variation, and a Pad \(\acute{e}\) e ´ approximation method to enhance search precision. Furthermore, MSHHO integrates a specialized random agent to ensure progressive search advancement. This paper compares the performance of MSHHO with other advanced algorithms using 23 benchmark functions and 8 engineering problems, providing a rigorous evaluation of their performance and rankings. In the economic scheduling problem of microgrid, MSHHO achieves an impressive reduction in operating costs by 16.31 \(\%\) % , demonstrating its superiority.