Optimal allocation of phase shifting transformer with uncertain wind power based on dynamic programming

被引：3

作者：

Fu, Kang ^{[1
]}

Du, Zhaobin ^{[1
,2
]}

Li, Feng ^{[3
]}

Li, Zuohong ^{[3
]}

Xia, Chengjun ^{[1
,2
]}

机构：

[1] South China Univ Technol, Guangzhou, Peoples R China

[2] Guangdong Prov Key Lab Intelligent Operat & Contro, Guangzhou, Peoples R China

[3] Grid Planning & Res Ctr Guangdong Power Grid Corp, Guangzhou, Peoples R China

来源：

FRONTIERS IN ENERGY RESEARCH | 2023年 / 10卷

关键词：

phase shifting transformer; dynamic programming; optimal allocation; wind power uncertainty; non-dominated sorting genetic algorithm II; FACTS DEVICES; HIGH PENETRATION; OPTIMAL LOCATION; PLACEMENT; SELECTION; SYSTEMS;

D O I：

10.3389/fenrg.2022.1003315

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Phase Shifting Transformer (PST) can help improve the power flow distribution of the transmission section, which can increase the wind power consumption of the grid. In order to adapt the PST allocation to the grid evolution, this paper presents a dynamic programming method to allocate PST in each planning stage of the grid optimally. The optimal allocation model of PST under a single grid seeks to maximize the wind power consumption and the Total Transfer Capacity (TTC) between areas. A calculation method for TTC of grids containing PST and wind power is proposed. The Non-Dominated Sorting Genetic Algorithm II (NSGA2) is used to solve the Pareto sets under each planning stage of the grid. Then, the optimal planning path of PST is derived based on dynamic programming. The superiority of the proposed method is demonstrated by comparing the IEEE-118 system results of dynamic and static programming.

引用

页数：14

共 30 条

[1]

[Anonymous], 2017, Getting wind and sun onto the grid: A manual of policy makers

[2]

Bandara H. M. M. T., 2019, 2019 International Conference on Advancements in Computing (ICAC), P68, DOI 10.1109/ICAC49085.2019.9103404

[3] Correlated wind-power production and electric load scenarios for investment decisions [J].

Baringo, L. ;

Conejo, A. J. .

APPLIED ENERGY, 2013, 101 :475-482

[4]

Blumsack S., 2006, THESIS

[5]

Brilinskii Andrei S., 2020, 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). Proceedings, P1176, DOI 10.1109/EIConRus49466.2020.9039410

[6] Optimal Selection of Phase Shifting Transformer Adjustment in Optimal Power Flow [J].

Ding, Tao ;

Bo, Rui ;

Bie, Zhaohong ;

Wang, Xifan .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2017, 32 (03) :2464-2465

[7] Optimal location of multi-type FACTS devices in a power system by means of genetic algorithms [J].

Gerbex, S ;

Cherkaoui, R ;

Germond, AJ .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2001, 16 (03) :537-544

[8] Optimal Placement of Multiple-Type FACTS Devices to Maximize Power System Loadability Using a Generic Graphical User Interface [J].

Ghahremani, Esmaeil ;

Kamwa, Innocent .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (02) :764-778

[9] Determination of Critical Factors for Optimal Positioning of Phase-Shift Transformers in Interconnected Systems [J].

Hadzimuratovic, Semir ;

Fickert, Lothar .

PROCEEDINGS OF THE 2018 19TH INTERNATIONAL SCIENTIFIC CONFERENCE ON ELECTRIC POWER ENGINEERING (EPE), 2018, :54-59

[10] Selection of optimal number and location of thyristor-controlled phase shifters using genetic based algorithms [J].

Ippolito, L ;

Siano, P .

IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 2004, 151 (05) :630-637

← 1 2 3 →