Data-driven energy management of isolated power systems under rapidly varying operating conditions

Abstract

We propose an energy management algorithm for isolated industrial power systems that integrate uncertain renewable generation and energy storage. The proposed strategy is designed to ensure sustainable and cost-effective operations by managing the energy flows in the grid, and is structured so to cope with: (1) high levels of renewable power penetration, and (2) load profiles characterized by non-smooth patterns and irregular events (i.e., events such as those occurring from connections/disconnections of large scale equipment, or from large wind speed ramps). The proposed algorithm leverages a stochastic economic model predictive control (MPC) scheme capable of dealing simultaneously with the dispatch and scheduling of the local generation units. More precisely, the scheme embeds a mixed-integer linear programming (MILP) optimal control policy formulation together with a stochastic programming approach. Moreover, the optimization problem accounts for multiple techno-economical objectives, such as minimization of operational costs, battery degradation, and non-utilized energy. We test the algorithm on a case study of an isolated offshore Oil & Gas platform producing energy onsite with conventional gas turbines and a local wind farm, while integrating a battery energy storage system. The results show that the proposed approach can issue ensemble predictions that successfully capture the potential irregular variations just by using recent past information of the associated random variable, even when no particular sudden events are anticipated in the near-future (i.e., step changes/trend reversals). In this way, the approach provides useful future information for the optimal management of the grid. This effect is numerically quantified via simulations that compare the performance of the proposed stochastic optimization approach against its deterministic MPC version in several realistic operating conditions. The empirical results suggest that the stochastic version leads to better scheduling of the conventional generators, with up to 12.86% reductions of the operating cost, 2.56% reduction in fuel consumption and emissions, and 35.29% reduction in status transitions (on/off) of the gas turbines, while keeping dumped energy and battery degradation as low as possible.