Data-Driven Learning and Load Ensemble Control

doi:10.1016/j.epsr.2020.106780

Data-Driven Learning and Load Ensemble Control

2020-12-01

5

OA

AI

DOI：10.1016/j.epsr.2020.106780

Original

Save

View PDF

Abstract

En 中文

Demand response (DR) programs aim to engage distributed small-scale flexible loads, such as thermostatically controllable loads (TCLs), to provide various grid support services. Linearly Solvable Markov Decision Process (LS-MDP), a variant of the traditional MDP, is used to model aggregated TCLs. Then, a model-free reinforcement learning technique called Z-learning is applied to learn the value function and derive the optimal policy for the DR aggregator to control TCLs. The learning process is robust against uncertainty that arises from estimating the passive dynamics of the aggregated TCLs. The efficiency of this data-driven learning is demonstrated through simulations on Heating, Cooling & Ventilation (HVAC) units in a testbed neighborhood of residential houses.

Keywords:

Markov Decision Process

Thermostatically Controlled Loads

Z-learning

Linearly Solvable MDP

TCL ensemble

Data-Driven Learning and Load Ensemble Control

Abstract

AI Summary

Journal

Organization