In this chapter, we will focus on the optimal participation strategies that electric vehicle load aggregators (EVLAs) can adopt when involved in incentive-based demand response (DR) programs, where EVs would be considered as controllable loads supplying load-reduction services at peak demand times. An MLMF Stackelberg game framework is established, in which DSO, EVLAs and EV users interact hierarchically. In this framework, EVLAs behave like the leaders who choose incentive prices to make the optimal profit through aggregating EV load reduction, whereas EV users play the followers who have to find the optimal amount of load they could reduce to acquire the optimal personal benefit from receiving the incentive, but also having some delay from charging. The existence and uniqueness of the Stackelberg equilibrium (SE) are rigorously proven by using the potential game theory, demonstrating that the non-cooperative game among EVLAs qualifies as an exact potential game with a unique Nash equilibrium (NE). To account for the inherent uncertainties in EV charging behaviors, stochastic models are introduced to characterize those key variables, e.g., plug-in time, charging duration, and energy demand by the use of truncated Chi-square and Gaussian distributions, respectively. Based on the numerical simulation of the community parking lot scenario, three EVLAs are considered to verify the feasibility of the proposed approach. The result demonstrates the significant effect of other influencing elements, including expected DR capacities, available load cutback subject to charger rating, user demand, and competitive equilibrium between EVLAs, on incentive plans for EVLAs. Sensitivity analysis further indicates that incentive prices tend to decrease as the number of EVs or participating EVLAs increases, while the higher DR capacity requirements may lead to increased incentive offers and reduced overall potential function values. This study provides a theoretical and practical foundation for designing an effective DR participation mechanism for EVLAs, highlighting the importance of strategic incentive design and competition management in smart grid environments.