Learning-Based Predictive Control via Real-Time Aggregate Flexibility

TL;DR

This paper introduces a real-time aggregate flexibility feedback method called MEF, combined with a penalized predictive control algorithm, to improve communication efficiency, computational speed, and cost-effectiveness in load coordination.

Contribution

It proposes the maximum entropy feedback (MEF) for real-time flexibility estimation and integrates it into a novel penalized predictive control (PPC) scheme, enhancing existing model predictive control methods.

Findings

PPC outperforms classical MPC in simulations.

MEF enables efficient communication without detailed load states.

PPC achieves lower costs under certain conditions.

Abstract

Aggregators have emerged as crucial tools for the coordination of distributed, controllable loads. To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator. However, most of existing aggregate flexibility measures often are slow-timescale estimations and much less attention has been paid to real-time coordination between an aggregator and an operator. In this paper, we consider solving an online optimization in a closed-loop system and present a design of real-time aggregate flexibility feedback, termed the maximum entropy feedback (MEF). In addition to deriving analytic properties of the MEF, combining learning and control, we show that it can be approximated using reinforcement learning and used as a penalty term in a novel control algorithm -- the penalized predictive control (PPC), which modifies vanilla model predictive control (MPC). The benefits of our scheme are (1). Efficient Communication. An operator running PPC does not need to know the exact states and constraints of the loads, but only the MEF. (2). Fast Computation. The PPC often has much less number of variables than an MPC formulation. (3). Lower Costs. We show that under certain regularity assumptions, the PPC is optimal. We illustrate the efficacy of the PPC using a dataset from an adaptive electric vehicle charging network and show that PPC outperforms classical MPC.