Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level

Due to the growth of intermittent generation and flexible demand, the difference between real metered load profiles and predicted profiles has increased significantly. This has caused a higher cost to suppliers as they have to mitigate the errors by using costly fast response generators or buying expensive energy from other suppliers. Improving load forecast accuracy is an alternative to reduce the difference and consequently the costs, but it relies on large quantities of historical load data which is not necessarily available. This paper utilises a novel control strategy for energy storage systems to mitigate forecast errors for suppliers. This results in energy cost savings and the Use-of-System (UoS) savings. In order to test the charging/discharging strategies and quantify the economic benefits, a case study has been conducted by utilizing smart metering data. The case study has produced a 33.2% cost reduction in the energy cost savings.