US 12,136,817 B2
Method of accessing dynamic flexibility for virtual power plant
Wenchuan Wu, Beijing (CN); Siyuan Wang, Beijing (CN); Hongbin Sun, Beijing (CN); Bin Wang, Beijing (CN); and Qinglai Guo, Beijing (CN)
Assigned to TSINGHUA UNIVERSITY, Beijing (CN)
Filed by TSINGHUA UNIVERSITY, Beijing (CN)
Filed on Sep. 1, 2021, as Appl. No. 17/446,642.
Claims priority of application No. 202011341035.3 (CN), filed on Nov. 25, 2020.
Prior Publication US 2022/0173593 A1, Jun. 2, 2022
Int. Cl. H02J 3/28 (2006.01); G06F 17/11 (2006.01); H02J 3/38 (2006.01)
CPC H02J 3/28 (2013.01) [G06F 17/11 (2013.01); H02J 3/381 (2013.01); H02J 2300/24 (2020.01); H02J 2300/28 (2020.01)] 17 Claims
OG exemplary drawing
 
1. A method of optimizing dynamic flexibility for a virtual power plant within a power distribution system, the virtual power plant comprising a plurality of physical resources in an electrical grid that are networked to provide a virtual power plant network, characterized in that the method comprises the following steps:
A. setting an output power constraint condition for each of the plurality of physical resources, the plurality of physical resources including at least one of a gas turbine, an energy storage device, a photovoltaic power generation device, a wind generator device, a demand-side responsive heating load and an electric vehicle charging station, and building a distributed energy-resource model of a virtual power plant;
B. setting a voltage magnitude, a current and an injection power in the virtual power plant network, thereby obtaining a network constraint condition of the virtual power plant, and building a power flow model for the virtual power plant network;
C. defining the energy storage device, the demand-side responsive heating load and the electric vehicle charging station as energy-storage-type devices; defining the gas turbine, the photovoltaic power generation device and the wind generator device as generator-type devices; defining a decision variable vector consisting of output power of all distributed generation energy and resources, wherein a decision variable consisting of output active power of all energy-storage-type devices of the plurality of physical resources is PE, a decision variable consisting of output active power of all generator-type devices of the plurality of physical resources is PG, and a decision variable Q consisting of inactive power of the plurality of physical resources is defined; and extracting an operation constraint condition of energy-storage-type and generator-type distributed resources based on a Robust optimization method;
D. generating models of the plurality of physical resources, the models including equivalents of the energy-storage-type devices and equivalents of the generator-type devices and solving a constraint parameter respectively for the equivalent energy-storage-type devices and the equivalent generator-type devices based on a two-stage robust optimization algorithm according to a parameter in the distributed energy-resource model for the virtual power plant in the step A, a parameter of the power flow model for the virtual power plant network in the step B, and the operation constraint condition of energy-storage-type and generator-type distributed resources in the step C; and
E. obtaining an assessment result of flexibility for the virtual power plant based on the generated model and the constraint parameter in the step D, and
F. applying the assessment result to control the plurality of physical resources for optimized scheduling and grid resource participation.