Abstarct: Coordinated energy management of distributed energy resources (DERs) and price-responsive demands as a virtual power plant (VPP) has become an attractive issue in electricity industry. Uncertainty in stochastic DERs, energy prices and availability of microgrid components makes the energy management problem of VPP more complex. Equipped with smart grid technology, it is possible to monitor the VPP condition near real-time, and modify energy management decisions to deal with the uncertainties. In this thesis, a stochastic robust optimization is proposed to model the energy management problem of a VPP that contains solar power stations, storage units and price-responsive demands interconnected within a power microgrid. This way, uncertainties in continuous (i.e. energy price and solar generation) and discrete random variables (i.e. availability of microgrid components) are modeled by interval- and scenario-baxsed forecasting approaches, respectively. The performance of the proposed stochastic robust optimization is analyzed through a case study, and compared with the performance of deterministic approach and robust optimization. The results validate solution feasibility and good performance of the proposed stochastic robust optimization particularly in the case that contingency occurs in a day with highly volatile energy prices.