This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggregator considering uncertainties. The aggregator, which integrates power and capacity of small-scale prosumers and fexible community-owned devices, trades electric energy in the day-ahead (DAM) and real-time energy markets (RTM), and trades reserve capacity and deployment in the reserve capacity (RCM) and reserve deployment markets (RDM). The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules, including minimum ofer/bid size and minimum delivery duration. A combination approach of stochastic programming (SP) and robust optimization (RO) is used to model diferent kinds of uncertainties, including those of market price, power/demand and reserve deployment. The risk management of the aggregator is considered through conditional value at risk (CVaR) and fuctuation intervals of the uncertain parameters. Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in diferent markets, reserve regulations, and risk preferences.