Virtual power plants (VPP) can aggregate multiple heterogeneous distributed energy resources (DER) to flexibly participate in the energy market. However, because of the uncertainty of bidding strategies of market participants, VPPs face potential risks of bidding failure in the day-ahead energy market. To solve the problem of VPPs’ optimal bidding strategy given the uncertainty of electricity price and quantity in the multiple competitive electricity market, a VPP flexible segmented bidding strategy is proposed. First, VPPs’ aggregated regulation capacity estimation method is constructed based on the operational characteristics of DERs, and a flexible segmented bidding quantity range of VPP is proposed considering power balance demand. Then, a VPPs’ day ahead energy market bidding model based on the Stackelberg game is established to realize the maximization of VPP profit and social welfare. Finaly, strong duality theory and the ‘big-M’ method are introduced to transfer the equilibrium problems with equilibrium constraints (EPEC) into a mixed integer linear program (MILP). The results of case studies indicate that the adoption of the flexible segmented bidding strategy in the day ahead electricity market can fully exploit VPP regulation capacity, ensure the effective bidding of electricity quantity demand, and increase VPP profit and social benefit.