Adaptive Robust Recourse-Based Bidding Strategy and Capacity Allocation of PV-WT-BES Owning Prosumers Under Uncertainties

Abstract

This article presents an adaptive robust co-optimization for capacity allocation and bidding strategy of a prosumer equipped with photovoltaic system (PV), wind turbine (WT), and battery energy storage (BES). The uncertainties of load and PV/WT productions are modeled through controllable user-defined polyhedral uncertainty sets. The proposed co-optimization determines the optimal capacity of PV-WT-BES, while maximizing prosumer's benefit by 1) optimal self-scheduling of PV-WT-BES, and 2) effective interactions with grid through optimal buying/selling bids under uncertainties. In previous min-max-min robust models, it was not possible to characterize bidding strategy binary variables as recourse decisions which was due to the use of duality theory in solving the inner max-min problem (duality theory is weak and nontractable in the presence of binary variables). In this study, block coordinate descent (BCD) method is used to solve the inner max-min problem by means of Taylor series instead of transforming it into a single-level max problem by duality theory. As a result, prosumer's bidding status (indicated by binary variables) can be successfully modeled as recourse decisions, which make the obtained solutions more realistic and robust. Linearization of the dualized inner problem is also avoided as Lagrange multipliers are eliminated. A post-event analysis is developed to avoid over/under conservative solutions and to determine the optimal robust settings of the model. A comprehensive case study is conducted for an industrial prosumer. To illustrate the effectiveness of the proposed BCD robust model, its long-term performance is compared with conventional dual-based models in the literature. Results show 10% long-term cost reductions when using the proposed model under uncertainties.