Data-Driven Learning and Load Ensemble Control

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. Th...

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Bibliographic Details
Published in:Electric power systems research 2020-12, Vol.189, p.1
Main Authors: Hassan, Ali, Deka, Deepjyoti, Chertkov, Michael, Dvorkin, Yury
Format: Article
Language:English
Subjects:
Cooling
Direct reduction
Houses
HVAC
Learning
Markov analysis
Markov processes
Passive dynamics
Residential areas
Stability
Stress concentration
Support services
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Summary: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.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2020.106730