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\begin{document}

\title{Optimization of Multi-energy Complementary Power Generation System
Configuration Based on Particle Swarm Pptimization}



\author[1]{Dongni Wei}%
\author[1]{Zelong Zhang}%
\author[1]{Weiqi Zhang}%
\author[1]{Yan Yang}%
\author[1]{Zhao Yang}%
\author[2]{Zeqi Ge}%
\author[2]{Xin Pan}%
\affil[1]{State Grid Ningxia Electric Power Co LTD. Economic and technological research institute}%
\affil[2]{North China Electric Power University}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
The multi-energy complementary power generation system with ``wind-wind
fire storage'' as a typical representative is an important path to
promote the symbiotic and complementary collaborative development of
thermal power and new energy. Under the background of the construction
of new power system and the integration of wind-wind, fire and storage,
the scientific and rational configuration optimization of multi-energy
complementary power generation system has become an important
prerequisite for its sustainable development. In this paper, a two-layer
optimization model for multi-energy complementary power generation
system configuration based on particle swarm optimization is proposed.
The outer layer optimization aims to maximize the net income of the
wind-wind storage power generation system, and the inner layer
optimization aims to minimize the carbon emission of thermal power
units, so as to realize the balance between environmental benefits and
economic benefits of the multi-energy complementary power generation
system. The research results provide a reference for the planning and
configuration of wind-wind-fire storage complementary power generation
system.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Optimization of Multi-energy Complementary Power Generation System Configuration Based on Particle Swarm Pptimization.docx`  available at \url{https://authorea.com/users/752255/articles/723106-optimization-of-multi-energy-complementary-power-generation-system-configuration-based-on-particle-swarm-pptimization}

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