Optimal scheduling of hydro-thermal power systems considering the flood risk of cascade reservoirs

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. Fi...

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Bibliographic Details
Published in:Engineering optimization 2017-08, Vol.49 (8), p.1299-1316
Main Authors: Yang, Hongming, Zhang, Lei, Meng, Ke, Xu, Jiangping, Lai, Mingyong, Dong, Zhao Yang
Format: Article
Language:English
Subjects:
Approximation
Cascade reservoirs
Computer simulation
conditional value at risk
Construction
Extreme value theory
Flood control
Flood insurance
flood prevention
Floods
Global warming
hydrothermal power system
Inflow
Mathematical analysis
Monte Carlo simulation
Normal distribution
optimal scheduling
Optimization
Pareto optimum
Reservoirs
Risk levels
Safety margins
Scheduling
Thermoelectricity
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Summary: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.
ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2016.1245537