US 12,272,950 B2
Method and device for optimal power flow calculation in power systems based on generalized Nash equilibrium
Lei Chen, Hangzhou (CN); Shufeng Dong, Hangzhou (CN); Qiang Yang, Hangzhou (CN); Jun Lyu, Beijing (CN); Weiwei Xu, Hangzhou (CN); Yihui Sun, Hangzhou (CN); Chongyou Xu, Hangzhou (CN); Jingen Song, Hangzhou (CN); Weifeng Xu, Hangzhou (CN); and Jun Huang, Hangzhou (CN)
Assigned to STATE GRID ZHEJIANG ELECTRIC POWER CO., LTD, Hangzhou (CN); and Zhejiang University, Zhejiang (CN)
Filed by STATE GRID ZHEJIANG ELECTRIC POWER CO., LTD, Zhejiang (CN); and Zhejiang University, Zhejiang (CN)
Filed on Dec. 13, 2023, as Appl. No. 18/538,149.
Claims priority of application No. 202211599868.9 (CN), filed on Dec. 14, 2022.
Prior Publication US 2024/0213776 A1, Jun. 27, 2024
Int. Cl. H02J 3/06 (2006.01)
CPC H02J 3/06 (2013.01) [H02J 2203/20 (2020.01)] 10 Claims
OG exemplary drawing
 
1. A method for optimal power flow calculation in power systems based on generalized Nash equilibrium, the power systems including a distribution network and microgrids which are physically coupled but managed independently, the method being performed by a processor of a system for optimal power flow calculation, comprising:
separately constructing a first optimal power flow model corresponding to the distribution network and a second optimal power flow model corresponding to each microgrid, in cases where the optimal power flow calculation is to be done by the processor for the power systems, wherein the first optimal power flow model is constructed using the processor based on a first objective function corresponding to the distribution network, and the second optimal power flow model corresponding to each microgrid is constructed using the processor based on a second objective function corresponding to the microgrid, wherein the first objective function corresponding to the distribution network is based on a control variable, a first state variable, and a boundary node state variable of the distribution network, wherein the control variable of the distribution network is active and reactive power of a generator in the distribution network, the first state variable of the distribution network is a voltage and phase angle of nodes in the distribution network, excluding boundary nodes, and the boundary node state variable of the distribution network is a voltage and phase angle of boundary nodes of the distribution network, and wherein the second objective function corresponding to the microgrid is based on a control variable, a second state variable, and a boundary node state variable of the microgrid, wherein the control variable of the microgrid is active and reactive power of a generator in the microgrid, the second state variable of the microgrid is a voltage and phase angle of nodes in the microgrid, excluding boundary nodes, and the boundary node state variable of the microgrid is a voltage and phase angle of boundary nodes of the microgrid;
constructing a third optimal power flow model using the processor which is based on the first optimal power flow model, each second optimal power flow model, and a preset boundary coupling constraint condition, the third optimal power flow model being a non-cooperative generalized Nash game model, wherein the preset boundary coupling constraint condition includes a constraint between the boundary node state variable of the distribution network and the boundary node state variable of each microgrid, and a constraint between a boundary node power injection of the distribution network and a boundary node power injection of each microgrid;
using the processor in determining a generalized Nash equilibrium solution of the distribution network and each microgrid in a non-cooperative state corresponding to the third optimal power flow model;
setting a collaborative objective function carried out by the processor, and corresponding to the distribution network and each microgrid in a cooperative state, the collaborative objective function being obtained by adding the first objective function of the distribution network to the second objective function of each microgrid;
determining a generalized Nash equilibrium constraint condition in the non-cooperative state based on the generalized Nash equilibrium solution;
constructing a fourth optimal power flow model based on the collaborative objective function in the cooperative state and the generalized Nash equilibrium constraint condition in the non-cooperative state;
determining a Pareto optimal solution corresponding to the fourth optimal power flow model; and
adjusting control variables including output power of generators and tap settings of controllable transformers in the distribution network and each microgrid in the power systems based on the fourth optimal power flow model by transmitting control signals to the controllable transformers in the distribution network and each microgrid in the power systems.