Chance constrained dynamic optimisation method for AGC units dispatch considering uncertainties of the offshore wind farm. Issue 16 (14th January 2019)

• Record Type: Journal Article
• Authors: Zhao, Xia; Ye, Xiaobin; Yang, Lun; Zhang, Rongrong; Yan, Wei
• Is Part Of: Journal of engineering. Volume 2019:Issue 16(2019)
• Page Start: 2112
• Page End: 2119
• Publication Date: 2019-01-14
• Subjects: power generation control -- evolutionary computation -- power generation dispatch -- power generation economics -- power generation scheduling -- power generation reliability -- stochastic processes -- wind power -- offshore installations -- wind power plants -- power grids -- optimisation; additional offshore wind power generation -- hybrid algorithm -- stochastic AGC dispatch method -- dynamic optimisation method -- AGC units -- offshore wind farm -- continuing growth -- power grid -- high‐voltage DC -- great challenges -- automatic generation control -- power system -- dynamic dispatch -- control performance standards -- economic dispatch -- existing DDA model -- uncertain forecasting error -- offshore wind power output -- novel stochastic DDA model -- chance‐constrained programming -- random offshore wind power forecasting error -- evolutionary programming algorithm -- point estimate method; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8558
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : The continuing growth of an offshore wind farm integrated into a power grid via high‐voltage DC has posed great challenges for automatic generation control (AGC) of the power system. To address these challenges, a new concept of 'dynamic dispatch of AGC units (DDA)' under control performance standards from the view of economic dispatch (ED) has been proposed in the previous work, and proved to be an effective technique to co‐operate the AGC units with different ramping rates and to fill the gap between ED and AGC. However, the existing DDA model is deterministic in nature, which can hardly deal with the uncertain forecasting error of the offshore wind power output. A novel stochastic DDA model based on chance‐constrained programming is proposed considering a random offshore wind power forecasting error, and a hybrid algorithm combining the evolutionary programming algorithm and point estimate method is then developed to solve the stochastic model. Numerical results from a two‐area test system with additional offshore wind power generation demonstrate the accuracy and computation efficiency of the hybrid algorithm and the benefits offered by the stochastic AGC dispatch method.