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Renewable energy sources and energy storage systems present specific challenges to the traditional optimal power flow (OPF) paradigm. First, storage devices require the OPF to model charge/discharge dynamics and the supply of generated power at a later time. Second, renewable energy sources necessitate that the OPF solution accounts for the control of conventional power generators in response to errors of renewable power forecast, which are significantly larger than the traditional load forecast errors. This paper presents a sparse formulation and solution for the affinely adjustable robust counterpart (AARC) of the multi-period OPF problem. The AARC aims at operating a storage portfolio via receding horizon control; it computes the optimal base-point conventional generation and storage schedule for the forecasted load and renewable generation, together with the constrained participation factors that dictate how conventional generation and storage will adjust to maintain feasible operation whenever the renewables deviate from their forecast. The approach is demonstrated on standard IEEE networks dispatched over a 24-h horizon with interval forecasted wind power, and the feasibility of operation under interval uncertainty is validated via Monte Carlo analysis. The computational performance of the proposed approach is compared with a conventional implementation of the AARC that employs successive constraint enforcement.Index Terms-Energy storage, integer linear programming, optimal power flow, optimization methods.

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