Flexible combined cooling, heating, and power (CCHP) systems are effective in integrating wind sources. As an attractive, clean, and large‐scale energy storage technique, the advanced adiabatic compressed air energy storage (AA‐CAES) can store and generate both electricity and heating, and also provide cooling during expansion under certain conditions. Although AA‐CAES has immense potential in multi‐energy supply systems, CCHP dispatch with AA‐CAES and wind power generation (WPG) is yet to be systematically studied. In this study, the economic dispatch of an AA‐CAES system equipped with WPG is addressed. The AA‐CAES system is comprehensively modelled by considering its thermal characteristics, air‐temperature changes due to heating exchange, air storage constraint, and other factors, particularly the heat supply to the air for expansion, which is a key factor that influences the cooling supply. Subsequently, the cooling, heating, and power of the AA‐CAES system are dispatched to minimise the operating cost under different supply modes. In conclusion, the proposed method is demonstrated using an integrated energy system in an industrial park, and the operation cost of the AA‐CAES system is minimised. The numerical results demonstrate that the participation of AA‐CAES in CCHP dispatch can curtail WPG and reduce operation costs. The economics of the different supply modes of AA‐CAES are also discussed.

In recent years, the optimal scheduling of multienergy has become the focus of the research. Against this background, this paper builds a model of a multienergy flow system of cooling, heating, and power, and advanced adiabatic compressed air energy storage (AA-CAES) is introduced to smooth wind power generation (WPG) and supply heating/cooling energy. Simulated annealing algorithm (SAA) is employed to energy-saving scheduling of the system with “exergy assessment” method. The energy-saving index and the exergy efficiency are compared in different cases. SAA is compared with the particle swarm optimization (PSO) algorithm in solving the optimal scheduling strategy. The cooling, heating, and power demands of an industrial park and WPG in typical days are employed to the simulation. The scheduling results of exergy input of SAA are far less than those of PSO in typical days of different seasons. The multienergy system without AA-CAES is also modeled, and energy-saving economic scheduling is carried out. The exergy efficiency of the system with AA-CAES is between 38% and 58% while the exergy efficiency of the system without AA-CAES is merely between 27% and 48%.