Saturation-aware robust optimal operation control of microgrids based on minimum-regret optimization
This work is significant for microgrid operators and energy managers, offering a method to ensure stable and optimal operation in the face of significant renewable energy and load uncertainties.
This paper addresses the challenge of robust optimal operation control in microgrids with high renewable energy penetration, aiming to maintain optimal operation despite uncertainties in renewable infeed and load demand. It proposes a minimum-regret robust model predictive control (MPC) problem, demonstrating its viability through a case study.
This paper studies robust optimal operation control problems for microgrids with a high share of renewable energy sources. The main goal is to ensure an optimal operation in the presence of a wide range of scenarios of uncertain infeed of renewable sources and uncertain load demand. We formally state a minimum-regret robust model predictive control (MPC) problem and address it by making effective use of a hierarchical microgrid control structure. In detail, we consider an enhanced primary control layer composed of droop control and an autonomous limitation of power and energy. We prove that this enables us to use constant power setpoints to achieve an optimal operation under certain conditions. To obtain a tractable controller, we then combine the abovementioned constant saturation-aware setpoints with an energy management system, which solves a robust unit commitment problem within a model predictive control framework. In a case study, we finally demonstrate the viability of the control design.