A fully adaptive distributionally robust multistage framework based on mixed decision rules for wind-thermal system operation under uncertainty

The growing integration of renewable energy into power systems offers opportunities for achieving low-cost and sustainable energy supplies. However, its intermittency poses technical challenges, necessitating flexible and reliable decision-making methods. This study aims to develop a framework to enhance the integration of wind power while ensuring system reliability and minimizing costs. A fully adaptive distributionally robust multistage framework is proposed, leveraging mixed decision rules to enable dynamic and efficient use of quick-start units and generation dispatch. The improved mixed decision rules expand the feasible region and handle higher dimensional variables, are first introduced in such problem. Advanced optimization techniques are employed to reformulate the framework into mixed integer linear programming, ensuring computational tractability. The introduction of improved mixed decision rules with distributionally robust optimization and the solvable reformulation of the framework highlight the novelty of this work. Case studies on IEEE test systems demonstrate the framework’s superiority over traditional models by increasing wind power penetration, reducing fossil fuel consumption, and providing feasible and optimal solutions in uncertain environments.