Paper Info
Title
Demand response with moving horizon estimation of individual thermostatic load states from aggregate power measurements
Abstract
We present an optimization-based state estimation method that allows us to estimate the states of individual thermostatically controlled loads (TCLs), such as air conditioners and space heaters, from measurements of the power consumption of small aggregations of TCLs. The state estimator can be used together with a controller to provide ancillary services to power systems such as frequency control. The main advantage of this method is that it is designed to work with existing communication infrastructure. We assume that aggregate power measurements are available from distribution substations every few seconds, while TCL state measurements are available from smart meters only every 20 minutes. We model TCLs as hybrid systems and propose a moving horizon state estimator (MHSE), which is formulated as a mixed-integer linear program. We demonstrate the performance of the MHSE in two case studies: (a) estimation of TCL states in the absence of external control actions, and (b) a power tracking problem with closed-loop control using broadcast control inputs. To demonstrate the robustness of the method, we conduct a parametric analysis with respect to aggregation size and diversity, process noise characteristics, and control trajectory characteristics. The results show that the method generally provides accurate estimates of TCL states, resulting in improved controller performance in most cases, and is implementable in real-time with reasonable computational power.
Year
DOI
Venue
2014
10.1109/ACC.2014.6859068
American Control Conference
Keywords
Field
DocType
closed loop systems,frequency control,load regulation,optimisation,power control,power system state estimation,MHSE,TCLs,aggregate power measurements,broadcast control inputs,closed-loop control,demand response,frequency control,mixed-integer linear program,moving horizon state estimator,optimization-based state estimation method,parametric analysis,power tracking problem,thermostatic load states,thermostatically controlled loads,Estimation,Hybrid systems,Power systems
Control theory,Control theory,Computer science,Demand response,Electric power system,Robustness (computer science),Control engineering,Automatic frequency control,Linear programming,Hybrid system,Trajectory
Conference
ISSN
Citations 
PageRank 
0743-1619
3
0.44
References 
Authors
11
3
Name
Order
Citations
PageRank
Evangelos Vrettos1314.27
Johanna L. Mathieu212021.94
Goran Andersson321234.14