Abstract: The uncertainties of wind energy after the grid-connection of large-scale wind farms increase the difficulty in balancing cost and risk during the making of spinning reserve plans.To meet this challenge,first the randomness of wind power is analyzed by taking forecasting errors and real-time fluctuations of wind speeds and wind turbine faults into consideration.Then a multi-objective optimization model is developed with the spinning reserve cost expectation（spinning reserve capacity costs and outage costs considered）and conditional value at risk（CVaR）taken into account.The optimal front set of spinning reserve cost expectation and CVaR are obtained by the multi-objective immune optimization algorithm and the Monte Carlo scenario simulation.Finally,the spinning reserve plan is firmed up based on the fuzzy decision-making theory.The example shows that this multi-objective model is able to effectively find out the front set of cost expectations of spinning reserve and CVaR.Compared with the traditional model,the fuzzy risk decision-making model has expanded the effective range of the risk tolerance coefficient,while better reflecting the risk attitudes.The model considering CVaR has more advantages than that merely considering the cost expectation in terms of risk shunning,and it can be a risk management tool for decision-makers.