Optimal scheduling of hydro-thermal power systems considering the flood risk of cascade reservoirs. Issue 8 (3rd August 2017)

• Record Type: Journal Article
• Authors: Yang, Hongming; Zhang, Lei; Meng, Ke; Xu, Jiangping; Lai, Mingyong; Dong, Zhao Yang
• Is Part Of: Engineering optimization. Volume 49:Issue 8(2017)
• Page Start: 1299
• Page End: 1316
• Publication Date: 2017-08-03
• Subjects: Cascade reservoirs -- conditional value at risk -- flood prevention -- hydrothermal power system -- optimal scheduling; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2016.1245537
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: The increased flood risk linked to global warming affects the safety of cascade reservoirs, with direct effects on the stable operation of power systems. In this article, an optimal scheduling framework for hydro-thermal power systems considering the flood risk of cascade reservoirs is presented. First, the extreme value theory-based peak over threshold model is adopted to build a generalized Pareto distribution of extreme flood inflow for a single reservoir. Then, the Copula function is used to build a joint probability distribution function of extreme inflow for cascade reservoirs during a flood period. Based on the superior performance of the conditional value at risk (CVaR) in characterizing the tail risk of the cascade reservoir spillway safety margin, a CVaR constraint for cascade reservoir flood prevention is proposed, and a scheduling model for hydro-thermal power systems considering the flood prevention risk of head-dependent cascade reservoirs is presented. Secondly, Rockafeller and Uryasey reformulation and sample average approximation are employed to transform the proposed model with a CVaR constraint into a convex solvable optimization model. Finally, a modified IEEE 14-node system is used to verify the better performance of the proposed model than that of the models with the chance constraint and with the independent normal distribution of extreme flood inflow. The impacts of flood prevention confidence level and Monte Carlo sample size on the optimal scheduling results are analysed quantitatively.