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Genetic research

Improved Genetic Algorithm Optimizes Low-Carbon Operations in Integrated Energy Systems

Strategic Insights

— 1 min read

Research has made significant advancements in addressing global climate change and energy crises through the development of low-carbon integrated energy systems (IESs). A recent study proposed an improved genetic algorithm (IGA) to optimize the multi-objective low-carbon operations of IESs, aiming to minimize both operating costs and carbon emissions. The IGA incorporates circular crossover and polynomial mutation techniques, enhancing genetic diversity and feasibility of solutions.

Key Takeaways:

  • The IGA designed to optimize low-carbon operations of IESs successfully reduces equality constraint violations to below 0.3 kW, representing less than 0.2% deviation from the IES's power demand in each time slot.
  • The IGA achieves maximum 5% improvement in both operational cost reduction and carbon emission minimization objectives compared to the unimproved single-objective genetic algorithm.
  • The algorithm is reliable and practically applicable for multi-objective optimization in low-carbon IESs.
  • The IGA's performance is compared against a multi-objective genetic algorithm, a multi-objective particle swarm algorithm, and a single-objective genetic algorithm, demonstrating its superior performance.
  • The IGA is designed to preserve advantageous traits from the parent population, enhance genetic diversity, and minimize constraint violations.

Statistics:

  • Equality constraint violations reduced to below 0.3 kW, representing less than 0.2% deviation from the IES's power demand in each time slot.
  • Maximum 5% improvement in both operational cost reduction and carbon emission minimization objectives compared to the unimproved single-objective genetic algorithm.
  • 0.3 kW equality constraint violation threshold achieved by the IGA.
  • 0.2% deviation from the IES's power demand in each time slot achieved by the IGA.
  • 3 different IES scenarios used to test the effectiveness of the IGA.

Sources:

  • NewsRx. Data on Algorithms Described by Researchers at Guangdong Power Grid Co. Ltd. (Multi-Objective Optimization for the Low-Carbon Operation of Integrated Energy Systems Based on an Improved Genetic Algorithm). Life Science Weekly. May 27, 2025; p 760.
  • Energies. Multi-Objective Optimization for the Low-Carbon Operation of Integrated Energy Systems Based on an Improved Genetic Algorithm. 2025,18(9):2283. (Energies - http://www.mdpi.com/journal/energies).

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