Data-Driven Learning and Load Ensemble Control

Ali Hassan, Deepjyoti Deka, Michael Chertkov, Yury Dvorkin

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number106780
JournalElectric Power Systems Research
Volume189
DOIs
Publication statusPublished - Dec 2020
Externally publishedYes

Keywords

  • Linearly Solvable MDP
  • Markov Decision Process
  • TCL ensemble
  • Thermostatically Controlled Loads
  • Z-learning

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