Data-driven two-stage stochastic optimization model for short-term hydro-thermal-wind coordination scheduling based on the dynamic extreme scenario set

Abstract

A data-driven two-stage stochastic optimization model for solving short-term hydro-thermal-wind coordination scheduling problem with complicated time-coupled and space-related characteristics is proposed in this paper. The generation, emission and power fluctuation costs in the day-ahead scheduling stage and power regulation costs by hydro-thermal units, wind curtailment and load shedding costs in the real-time scheduling stage is formulated as the objective function of proposed model. Moreover, a dynamic extreme scenario generation and reduction method is introduced to deal with wind power uncertainty. The relationship between hydropower output, volume, inflow, discharge and upstream water for the multi-reservoir cascaded hydro plants is converted into a series of linear constraints, and the practical constraints of prohibited operation zones for thermal units are taken into account. As a result, the proposed model can be transformed into a mixed-integer linear programming problem to solve. Finally, case studies are carried out on the modified IEEE 24-bus system comprising of six thermal units, a multi-chain cascade of four hydro plants and one wind farm, and simulation results verify the effectiveness of the proposed coordination scheduling model and method. • Two-stage stochastic optimization for hydro-thermal-wind system is proposed. • Dynamic extreme scenario set is constructed to describe wind power uncertainty. • Spatio-temporal coupling characteristics of cascaded hydro plants is considered. • A mixed-integer linear programming is built for solving the scheduling problem.