Multi-objective optimization of capacity and technology selection for provincial energy storage in China: The effects of peak-shifting and valley-filling

Abstract

To support long-term energy storage capacity planning, this study proposes a non-linear multi-objective planning model for provincial energy storage capacity (ESC) and technology selection in China. The model aims to minimize the load peak-to-valley difference after peak-shaving and valley-filling. We consider six existing mainstream energy storage technologies: pumped hydro storage (PHS), compressed air energy storage (CAES), super-capacitors (SC), lithium-ion batteries, lead-acid batteries, and vanadium redox flow batteries (VRB). The optimized results show that 1) the accumulated ESC in China could increase at an average annual rate of 14.4–17.6% in 2020–2035, reaching 271.1–409.7 GW in 2035 at a total cost of 34.9–41.6 trillion yuan. 2) With favorable technical development levels and application prospects, lithium-ion batteries will be the dominant technology in 2035 in all scenarios. SC has the fastest growth rate among the six technologies in most scenarios because of its flexible location and long lifetime. 3) In the BAU scenario, Xinjiang, Inner Mongolia, Hebei, Ningxia, and Qinghai are the provinces with the largest ESC in 2035 while the four municipalities of Chongqing, Beijing, Tianjin, and Shanghai, as well as Tibet, have the smallest. This layout depends on the differences in the installed capacity of renewable energy in the provinces.