To address the impact of the uncertainty of renewable energy output on scheduling plans

a robust scheduling method considering stochastic scenarios of renewable energy is proposed. Firstly

for the generation of stochastic scenarios of renewable energy output

the interval and temporal characteristics of errors are taken into account. Kernel density estimation and Markov chain modeling are employed

followed by an improved K-means algorithm for scenario reduction

to generate day-ahead stochastic scenarios of renewable energy output for computation. Secondly

to tackle the issue of intraday deviations of renewable energy output from predicted values

a robust scheduling model incorporating stochastic scenario constraints is constructed

which considers both stochastic scenario constraints and scenario transition constraints. Furthermore

due to the large number of stochastic scenarios leading to an oversized robust scheduling model

a solution method based on stochastic scenario feasibility verification is proposed. Finally

the economic efficiency

safety

and effectiveness of the proposed robust scheduling model and solution method are validated through case studies based on a provincial and regional power grid framework. The results demonstrate that the proposed robust scheduling model can effectively solve the unit commitment problem in power markets with high penetration of renewable energy

and the running time and solution efficiency of the proposed model’s solution method are acceptable in practical market applications.