Multi-Scenario Stochastic Optimization for Advanced Adiabatic Compressed Air Energy Storage Capacity Planning

With the increasing penetration of renewable energy, the traditional energy storage capacity planning method may become impracticable due to space-time asymmetry of electricity generation and consumption, and multiple random factors coupling. In this paper, a cogeneration microgrid with advanced adiabatic compressed air energy storage system (AA-CAES) is constructed to accomplish the energy cascade use in a complex environment, in this context, a capacity planning strategy of AA-CAES is proposed to catering the challenges of multiple random based on the Latin Hypercube Sampling (LHS) and K-means clustering algorithms. The numerical simulation is carried out to validate the result and illustrated the relationship between the system economy and the number of samples N and the clusters K. The result indicates that the operation and construction costs of the system are likely to be stable when the number of samples N is more than 8×10
⁴
and the number of clusters K is less than 5, the economy of the system is optimal at this time.