Neural Predictive Control for the Optimization of Smart Grid Flexibility Schedules

TL;DR

This paper introduces a Neural Predictive Control scheme that learns optimal control policies for smart grid scheduling, significantly reducing computation time while maintaining near-optimal solutions.

Contribution

It proposes a novel NPC approach that uses imitation learning to efficiently approximate optimal control in complex power systems.

Findings

Reduces control calculation time by an order of magnitude.

Achieves near-optimal control solutions in benchmark smart grid.

Validates effectiveness on both linear and nonlinear systems.

Abstract

Model predictive control (MPC) is a method to formulate the optimal scheduling problem for grid flexibilities in a mathematical manner. The resulting time-constrained optimization problem can be re-solved in each optimization time step using classical optimization methods such as Second Order Cone Programming (SOCP) or Interior Point Methods (IPOPT). When applying MPC in a rolling horizon scheme, the impact of uncertainty in forecasts on the optimal schedule is reduced. While MPC methods promise accurate results for time-constrained grid optimization they are inherently limited by the calculation time needed for large and complex power system models. Learning the optimal control behaviour using function approximation offers the possibility to determine near-optimal control actions with short calculation time. A Neural Predictive Control (NPC) scheme is proposed to learn optimal control policies for linear and nonlinear power systems through imitation. It is demonstrated that this procedure can find near-optimal solutions, while reducing the calculation time by an order of magnitude. The learned controllers are validated using a benchmark smart grid.