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Area of Science:

  • Energy Systems Engineering
  • Environmental Science
  • Computational Intelligence

Background:

  • Growing environmental concerns drive the shift towards renewable energy generation.
  • Instability in renewable sources and varied user demands necessitate optimized energy scheduling.
  • Building Integrated Energy Systems (BIES) offer a solution for managing diverse energy components.

Purpose of the Study:

  • To develop an energy scheduling optimization model for BIES.
  • To prioritize economic efficiency and minimize carbon emissions.
  • To address the challenges of renewable energy instability and demand fluctuations.

Main Methods:

  • Data collection and pre-processing for regional building conformance.
  • Implementation of an enhanced multi-objective genetic whale algorithm.
  • Integration of components: gas turbines, wind/solar modules, heat pumps, EVs, AC, and energy storage.

Main Results:

  • The enhanced whale algorithm demonstrated robust convergence and stability.
  • Optimized scheduling effectively balanced economic and environmental objectives.
  • Achieved average daily cost reduction of ~92.896 yuan and carbon emission reduction of ~0.091 tons.

Conclusions:

  • The proposed model and algorithm are effective for optimizing BIES operation.
  • Significant cost savings and environmental benefits were realized.
  • The approach promotes efficient energy management and reduces environmental impact.