Video surveillance requires storing massive amounts of video data, which results in the rapid increasing of storage energy consumption. With the popularization of video surveillance, green storage for video surveillance is very attractive. The existing energy-saving methods for massive storage mostly concentrate on the data centers, mainly with random access, whereas the storage of video surveillance has inherent workload characteristics and access pattern, which can be fully exploited to save more energy. A dynamic partial-parallel data layout (DPPDL) is proposed for green video surveillance storage. It adopts a dynamic partial-parallel strategy, which dynamically allocates the storage space with an appropriate degree of partial parallelism according to performance requirement. Partial parallelism benefits energy conservation by scheduling only partial disks to work; a dynamic degree of parallelism can provide appropriate performances for various intensity workloads. DPPDL is evaluated by a simulated video surveillance consisting of 60–300 cameras with $1920 \times 1080$ pixels. The experiment shows that DPPDL is most energy efficient, while tolerating single disk failure and providing more than 20% performance margin. On average, it saves 7%, 19%, 31%, 36%, 56%, and 59% more energy than a CacheRAID, Semi-RAID, Hibernator, MAID, eRAID5, and PARAID, respectively.