Energy Conversion and Management· 2026Q1
Fuel cell combined cooling, heating and power system: Energy management strategy and multi-objective optimization configuration considering economy and energy efficiency
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- Q1SCImago
- 2026year
Short summary
A novel Following Coordinated Thermal-Electric Load (FCTEL) strategy for fuel cell combined cooling, heating, and power (FC-CCHP) systems reduces daily costs by up to 5.35% and increases energy efficiency by up to 1.35% compared to existing methods.
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Key points
- The FCTEL strategy reduces daily costs by 5.35% and increases energy efficiency by 1.35% compared to FTL and FEL strategies.
- FCTEL achieves 99.4% of day-ahead predictive scheduling's energy coefficient and 98.0% of its exergy efficiency via real-time scheduling.
- A BP neural network and MOPSO algorithm identified optimal configurations, balancing cost and efficiency.
- The proposed framework effectively balances economic and energy performance for FC-CCHP systems.
AI-generated from the title and abstract; the full text is not read.
Abstract
This study investigates the fuel cell combined cooling, heating and power (FC-CCHP) system, and proposes a novel Following Coordinated Thermal-Electric Load (FCTEL) strategy to overcome the multi-load mismatching defects of conventional FTL and FEL strategies. Simulation results show that compared with FTL and FEL strategies, the FCTEL strategy reduces system daily cost by 5.35 % and 4.48 %, increases energy coefficient by 1.35 % and 1.02 %, and improves exergy efficiency by 2.54 % and 1.91 %, respectively; benchmarked against the mixed-integer-linear-programming-based day-ahead predictive scheduling (DAPS) strategy, the daily cost of FCTEL is only 3.4 % higher than that of DAPS, while its energy coefficient and exergy efficiency reach 99.4 % and 98.0 % of those of DAPS through real-time scheduling rather than day-ahead predictive scheduling. The performance ranking of the three strategies remains unchanged under load perturbations and electricity price variations, confirming the robustness of the proposed strategy to both load and price uncertainties. A high-precision BP neural network surrogate model is constructed and combined with the improved MOPSO algorithm to obtain the Pareto frontier, and TOPSIS is adopted to select optimal compromise configurations, the obtained scheme reduces cost by 4.53 % versus the efficiency-optimal solution, and increases energy coefficient and exergy efficiency by 6.90 % and 33.91 % versus the economic-optimal scheme. Furthermore, the Pareto frontier is divided into two typical regions with clear basis, providing differentiated configuration suggestions for residential scenarios with different efficiency and budget demands. Annual dynamic simulation verifies the system can adapt to seasonal load variations and realize smooth switching among different working conditions. The proposed strategy-coupled multi-objective optimization framework can effectively balance economic and energy performance, providing theoretical support and engineering guidance for FC-CCHP application.
The authors' abstract, as published at the source. Energy Conversion and Management, 2026 · DOI ↗
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Field: Energy Engineering and Power Technology
Energy Engineering and Power TechnologyEnergy