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Power of Ensemble Diversity and Randomization for Energy Aggregation
David Métivier1, Ilia Luchnikov2, Michael Chertkov3,2
1Center for Nonlinear Studies & T-4, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. metivier@lanl.gov.
Scientific Reports
|April 13, 2019
Summary
This study models diverse energy loads for demand response (DR) services. More regular load behavior leads to faster system recovery, similar to Landau damping in plasma physics.
Area of Science:
- Statistical physics applied to energy systems.
- Non-equilibrium thermodynamics of aggregated loads.
Background:
- Thermostatically controlled loads (TCLs) are aggregated for demand response (DR) services.
- Ensemble diversity in TCLs is modeled via inhomogeneous dynamics and Poisson switching rates.
Purpose of the Study:
- To analyze the trade-off between aggregator control and system dynamics in DR.
- To investigate the impact of load diversity on system recovery speed.
- To explore the relationship between disorder probability distributions (DPDs) and system mixing.
Main Methods:
- Statistical physics approach to model a non-equilibrium system of aggregated TCLs.
- Analysis of phase-space dynamics and control of switching rates (r).
- Investigation of four different disorder probability distributions (DPDs).
Main Results:
- Stronger regularity in DPDs leads to faster system mixing and recovery.
- Aggregator control (lowering rate r) impacts deterministic dynamics and recovery.
- System recovery dynamics show similarities to Landau damping in plasma physics.
Conclusions:
- Load diversity significantly influences the performance of DR services.
- Regularity in load behavior enhances the efficiency of energy system recovery.
- The study provides a physics-based framework for understanding aggregated TCLs in DR.
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