Operation optimization of combined cooling, heating, and power superstructure system for satisfying demand fluctuation

Improving the overall performance of combined cooling, heating, and power (CCHP) systems have received great attention from both industry and academia. The existing studies commonly address single structure CCHP systems giving less consideration to demand fluctuation. And its operations are often optimized by stochastic algorithms, such as Genetic Algorithm (GA), which in general has difficulty obeying equality and demand constraints in large-scale CCHP problems. This paper presents a novel superstructure CCHP system, and deterministic modelling and optimization approaches for CCHP operations. In the proposed superstructure system, power generator units produce electricity and waste heat, and this waste heat can drive multiple hot water heat exchangers and absorption chillers to provide heating and cooling energy to buildings, which utilizes the waste heat more flexibly. In case studies, a recently reported single structure CCHP system is optimized by using GA and our method firstly. Compared with the solutions given by GA that causes some deviations from the constraints of heat recovery unit, our method can avoid the deviations and provides feasible solutions. More importantly, in the condition of large energy demand, the superstructure CCHP system can achieve more energy saving (up to 12.8 %) than the single-structure CCHP system, which demonstrates the validity and efficiency of our proposed CCHP structure and optimization methods. (c) 2021 Published by Elsevier Ltd.

Automated summary

This paper introduces a novel superstructure CCHP system and uses deterministic modeling and optimization methods for CCHP operations, which outperforms traditional stochastic algorithms like Genetic Algorithm in adhering to demand constraints. Case studies demonstrate that the superstructure CCHP system achieves greater energy savings in large energy demand scenarios compared to single-structure CCHP systems.