Optimizing Combined Cooling, Heating, and Power Systems with Thermal Energy Storage

Strategic Insights -

Researchers from Shijiazhuang Tiedao University in Hebei, People's Republic of China, have developed an integrated framework for optimizing combined cooling, heating, and power (CCHP) systems coupled with thermal energy storage (TES). This framework aims to enhance operational efficiency, cost-effectiveness, and environmental sustainability. The study incorporates real-time optimization strategies, demand flexibility mechanisms, and dynamic adjustments to power generation units.

Key Takeaways:

  • The research team developed a modified Genetic Algorithm (MGA) to optimize CCHP systems, which incorporates chaotic initialization, variable population sizing, and adaptive local search techniques.
  • The proposed approach has been validated through simulation results, demonstrating notable improvements in energy efficiency, cost savings, and reductions in carbon emissions.
  • Performance comparisons indicate that MGA consistently outperforms particle swarm optimization (PSO) and the secretary bird optimization algorithm (SBOA), achieving a 2.2% and 1.65% reduction in the mean objective function value (MOFV), respectively.
  • The model also accounts for uncertainties associated with load forecasting, operational performance, and environmental factors, making it a robust and adaptable solution for optimizing CCHP systems.
  • In Scenario 2, operational costs were reduced by 0.41%, from 1234 to 1229 cents, highlighting MGA's superior optimization capability and computational efficiency.
  • The study has been funded by the National Natural Science Foundation of China (NSFC), National Key R&D Program of China, and Open Fund Project of Hebei Ocean Dynamics Process and Resource Environment Laboratory.

Statistics:

  • The optimized CCHP system achieved a 2.2% reduction in the mean objective function value (MOFV) compared to particle swarm optimization (PSO).
  • The MGA yielded faster results, achieving results 14.94% faster than PSO and 20.43% faster than SBOA.
  • The model reduced operational costs by 0.41% in Scenario 2, from 1234 to 1229 cents.
  • The study demonstrated notable improvements in energy efficiency, cost savings, and reductions in carbon emissions.

Sources:

  • Optimization of Combined Cooling, Heating, and Power Systems With Thermal Energy Storage Using a Modified Genetic Algorithm. Journal of Building Engineering, 2025;107.
  • NewsRx. New Mathematics Findings Has Been Reported by Investigators at Shijiazhuang Tiedao University (Optimization of Combined Cooling, Heating, and Power Systems With Thermal Energy Storage Using a Modified Genetic Algorithm). Energy Weekly News. August 8, 2025; p 554.