Abstract: The uncertainty of wind power output significantly impacts the stable operation of power systems.To address this, the Conditional Value-at-Risk (CVaR) model was applied to quantify the conditional risk caused by wind power output uncertainty.A three-objective optimization model for the joint dispatching of hydro-thermal-wind power systems was established, considering wind power risk, generation cost, and pollution emissions.The Pareto optimal solution set for this model was obtained using the multi-objective genetic algorithm (NSGA-Ⅲ).Furthermore, an improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) multi-attribute decision-making method was proposed to evaluate the dispatching schemes.The operational results demonstrated that: the improved TOPSIS method effectively resolves the rank reversal problem and enhances multi-attribute decision-making.Particularly under conditions of high wind power uncertainty and significant wind abandoning risk, the optimized scheme substantially improves the overall system benefit.The dispatching scheme set is ranked and optimized based on varying priorities of safety, economy, and environmental protection.Compared with the traditional TOPSIS method, the improved TOPSIS approach increases operational costs by only 0.07%, while reducing operational risk by 24% and carbon emissions by 2.25%, confirming its rationality and effectiveness in solving multi-attribute decision-making problems in hydro-thermal-wind power joint dispatching.