Aggregated modeling of thermostatic loads in demand response: A systems and control perspective

Kalsi, Karanjit; Chassin, Forrest S.; Chassin, David P.

doi:10.1109/cdc.2011.6160448

Cited by 35 publications

(28 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Proposition 1: Under Assumptions 1 and 2, for ν ∈ Q \ Q f \ Q 0 , the density function φ ν : R + × X → R + is defined piecewise according to (7), where φ 0 ν ≡ 0, on R + ×Ω 0 ν and φ j ν ,j = 1, 2, satisfies L ν [φ j ν ] = 0 subject to boundary conditions (11), (12), and (13).…”

Section: A Homogeneous Loads Aggregationmentioning

confidence: 99%

“…A key step of this approach is the development of an aggregate model that can accurately capture the collective dynamics of the load population. Existing works on aggregate modeling have been mainly focusing on Thermostatically Controlled Loads (TCLs) such as HVACs (Heating, Ventilation, and Air conditioning) and water heaters [10], [11], [12], [13], [14]. Most of these works assume that individual loads are described by first-order linear ordinary differential equations (ODEs).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Demand dynamics aggregation using hybrid systems

Zhang

Chang

2012

2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

View full text Add to dashboard Cite

Modeling the dynamical behavior of a large population of responsive loads is especially important to evaluate the effectiveness of various demand response programs. Existing works on aggregated load modeling mainly focus on Thermostatically Controlled Loads (TCLs) described by first-order linear ordinary differential equations. In this paper, we propose a unified hybrid system model with general nonlinear subsystem dynamics to describe individual load dynamics. Such a model can be used to represent both TCLs and deferrable loads such as washers, dryers and plug-in hybrid electric vehicles (PHEVs). An aggregate model is also developed based on the proposed hybrid system load model. Realistic simulations show that the aggregate model can accurately capture the collective dynamics for TCLs and deferrable loads. The results presented in this paper can be used as a general aggregate load modeling framework for the analysis and design of various demand response strategies.

show abstract

Section: A Homogeneous Loads Aggregationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Demand dynamics aggregation using hybrid systems

Zhang

Chang

2012

2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

View full text Add to dashboard Cite

show abstract

“…The thermal load or rather loss of it, is expressed by (17), which defines the cooling and heating duration (also rates in a population) and is linearly dependent of DT. Cooling and heating rates are also directly linked to duty cycle (D ¼ c/r þ c) [40]. Equation (18) is a natural outcome, since when the thermal demand changes power rating doesn't, what does is duty cycle, proportionally to the change of the thermal load.…”

Section: Ambient Temperaturementioning

confidence: 99%

Human in the loop heterogeneous modelling of thermostatically controlled loads for demand side management studies

Kleidaras

Kiprakis

Thompson

2018

Energy

View full text Add to dashboard Cite

Index Terms:Demand side management Demand response Thermostatically controlled loads Markov models a b s t r a c t Demand Response (DR) is a Smart Grid technology aiming to provide demand regulation for dynamic pricing and ancillary services to the grid. Thermostatically controlled loads (TCLs) are among those with the highest potential for DR. Some of the challenges in modelling TCLs is the various factors that affect their duty cycle, mainly human behaviour and external conditions, as well as heterogeneity of TCLs (load parameters). These add an element of stochasticity, with detrimental impact on the aggregated level. Most models developed so far use Wiener processes to represent this behaviour, which in aggregated models, such as those based on Coupled Fokker-Planck Equations (CFPE), have a negligible effect as "white noise". One of the main challenges is modelling the effect of external factors on the state of TCLs' aggregated population and their impact in heterogeneity during operation. Here we show the importance of those factors as well as their detrimental effect in heterogeneity using cold loads as a case study. A bottom up detailed model has been developed starting from thermal modelling to include these factors, real world data was used as input for realistic results. Based on those we found that the duty cycle of some TCLs in the population can change significantly and thus the state of the TCLs' population as a whole. Subsequently, the accuracy of aggregation models assuming relative homogeneity and based on small stochasticity (i.e. Wiener process with typical variance 0.01) is questionable. We anticipate similar realistic models to be used for real world applications and aggregation methods based on them, especially for cold loads and similar TCLs, where external factors and heterogeneity in time are significant. DR control frameworks for TCLs should also be designed with that behaviour in mind and the developed bottom up model can be used to evaluate their accuracy.

show abstract

“…This approach has been previously used in [14,20,55,59], among other works, to capture first-order transients without having to perform a heavily-detailed building simulation that would require an extensive previous knowledge of the buildings and their processes. Another advantage of having a relatively simple building model is that it also facilitates the real-time implementation in control systems, such as the optimization framework we propose in this work.…”

Section: Thermodynamic Modelsmentioning

confidence: 99%

“…The optimization problem is to choose the switching schedules (β(i, t)) and PV production schedule (E RES (t)) over the whole time horizon, such that either energy consumption (13) or energy cost (14) are minimized; the building temperatures (T in (i, t)) will not exceed the critical values determined by the end-users (12b); and local renewable energy exports from the microgrid to the distribution grid are curtailed (12c). The optimization problem is also subject to the physical constraints of local energy generation from renewables in the microgrid (12d).…”

Section: Problem Formulationmentioning

confidence: 99%

Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation

et al. 2016

View full text Add to dashboard Cite

This paper presents a demand response (DR) framework that intertwines thermodynamic building models with a genetic algorithm (GA)-based optimization method. The framework optimizes heating/cooling schedules of end-users inside a business park microgrid with local distributed generation from renewable energy sources (DG-RES) based on two separate objectives: net load minimization and electricity cost minimization. DG-RES is treated as a curtailable resource in anticipation of future scenarios where the infeed of DG-RES to the regional distribution network could be limited. We test the DR framework with a case study of a refrigerated warehouse and an office building located in a business park with local PV generation. Results show the technical potential of the DR framework in harnessing the flexibility of the thermal masses from end-user sites in order to: (1) reduce the energy exchange at the point of connection; (2) reduce the cost of electricity for the microgrid end-users; and (3) increase the local utilization of DG-RES in cases where DG-RES exports to the grid are restricted. The results of this work can aid end-users and distribution network operators to reduce energy costs and energy consumption.

show abstract

Aggregated modeling of thermostatic loads in demand response: A systems and control perspective

Cited by 35 publications

References 7 publications

Demand dynamics aggregation using hybrid systems

Demand dynamics aggregation using hybrid systems

Human in the loop heterogeneous modelling of thermostatically controlled loads for demand side management studies

Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation

Contact Info

Product

Resources

About