This paper presents a two-stage adaptive robust optimization approach to develop an optimal bidding strategy for a grid-connected solar photovoltaic (PV) plant with a coupled energy storage system (ESS). This study models the power flow through system elements as well as the exact interactions between the system and upstream network. The uncertainties of solar radiation, affecting the PV generation and market prices are characterized by bounded intervals in polyhedral uncertainty sets. A robust optimization is formed as a min-max-min problem characterizing both “here-and-now” and “wait-and-see” variables. This tri-level robust optimization is solved through a decomposition approach, where it is recast into a min master problem and a max-min subproblem. Unlike previous conventional robust optimization models, that utilise duality for solving the inner subproblem, a block coordinate decent (BCD) methodology is used in this study. Accordingly, instead of conducting duality theory, the subproblem is solved over a first-order Taylor series approximation of uncertainties. This results in a moderate computation/mathematical burden. Moreover, there is no need to linearize the dualized problem anymore, as no duality is conducted. Using BCD methodology in solving the robust optimization model also allows modelling binary variables as recourse actions, which differentiates this approach to conventional dual-based robust optimization models. An illustrative example is provided to demonstrate the performance of the proposed bidding strategy model. Refereed/Peer-reviewed