US 12,407,173 B2
Electrical power system and a multi-timescale coordinated optimization scheduling method therefor
Katayoun Rahbar, Singapore (SG); Sajitha Nair, Singapore (SG); Peng Peng, Singapore (SG); and Chuan Luo, Singapore (SG)
Assigned to UNIVERS PTE. LTD., Singapore (SG)
Appl. No. 18/282,116
Filed by UNIVERS PTE. LTD., Singapore (SG)
PCT Filed Mar. 2, 2022, PCT No. PCT/SG2022/050107
§ 371(c)(1), (2) Date Sep. 14, 2023,
PCT Pub. No. WO2023/167631, PCT Pub. Date Sep. 7, 2023.
Prior Publication US 2024/0275179 A1, Aug. 15, 2024
Int. Cl. H02J 3/46 (2006.01); H02J 3/00 (2006.01)
CPC H02J 3/466 (2020.01) [H02J 3/003 (2020.01); H02J 3/004 (2020.01); H02J 2300/24 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A multi-timescale coordinated optimization scheduling method for an electrical power system including a plurality of distributed energy resources (DERs), the method comprising:
performing, via cloud computing using a cloud optimization engine, a long-timescale optimization scheduling for the electrical power system based at least on forecast data to obtain a long-timescale operation planning data;
determining if the long-timescale operation planning data is available;
performing, via edge computing using a model predictive control engine, a mid-timescale optimization scheduling for the electrical power system by solving a first optimization problem based on the long-timescale operation planning data and measured data of the plurality of DERs to obtain a mid-timescale operation planning data when it is determined that the long-timescale operation planning data is available;
performing, via edge computing using the model predictive control engine, the mid-timescale optimization scheduling for the electrical power system by solving a second optimization problem based on forecast data and the measured data of the plurality of DERs to obtain the mid-timescale operation planning data when it is determined that the long-timescale operation planning data is not available, the second optimization problem is different from the first optimization problem;
performing, via edge computing using a real-time predictive control engine, at least close to real-time optimization scheduling for the electrical power system based on the mid-timescale operation planning data, the measured data of the plurality of DERs and grid signals of the electrical power system to obtain a short-timescale power setpoints for the plurality of DERs; and
sending the short-timescale power setpoints to the plurality of DERs and controlling the short-timescale power setpoints;
wherein the long-timescale is longer than the mid-timescale, and the mid-timescale is longer than the short-timescale.