Open AccessInterval voltage control method for transmission systems considering interval uncertainties of renewable power generation and load demand
Author(s) -
Zhang Cong,
Chen Haoyong,
Liang Zipeng,
Mo Weike,
Zheng Xiaodong,
Hua Dong
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.5419
Subject(s) - renewable energy , interval (graph theory) , computer science , electric power system , wind power , mathematical optimization , control theory (sociology) , voltage , grid code , ac power , power (physics) , engineering , control (management) , electrical engineering , mathematics , physics , combinatorics , quantum mechanics , artificial intelligence
Renewable energy sources provide an important means of reducing reliance on conventional fuels. However, some renewable energy resources such as wind and solar energies are intermittent, and their uncertainty threatens the operating security of the power grid. To solve this problem, this study proposes the use of intervals to model the power output of renewable energy resources and the power load demand, and accordingly develops an interval voltage control model, i.e. interval reactive power optimisation model. The proposed model considers the control modes of renewable energy power generators and can safeguard the security of power grids by ensuring that the voltages reside within established limits. An adaptive genetic algorithm is employed to solve the proposed model, where a newly developed interval power‐flow (IPF) calculation is used to solve the IPF equations, and penalty functions are applied to express inequality constraints. The proposed method is introduced in detail, and simulation results are presented to demonstrate its performance in comparison with a previously proposed interval voltage control method, as well as its applicability to large systems with various fluctuations of input data. The proposed approach provides robust convergence, obtains lower system power losses, and substantially reduces the computation time.