Abstract
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.
Highlights
As more intermittent renewable and distributed energy comes onto the grid, old generation resources are retired and costly new plant expenditures are avoided
This paper focuses on examining the importance of external factors and creating a realistic model using cold loads as a case study
Defrost heaters are typically on for less than 5% of the time [8,24], yet their demand during this time is usually considerably higher than that of the cold load's operation (480 W [8]). They operate a few times per day, with daily consumption around 0.35 kWh [8] and are more suited as deferrable loads rather than as flexible. They don't follow the models described in this paper, whose focus is a realistic model to represent the dynamics of Thermostatically controlled loads (TCLs) for dispatchable Demand Response (DR)
Summary
As more intermittent renewable and distributed energy comes onto the grid, old generation resources are retired and costly new plant expenditures are avoided. Transmission congestion in Medium Voltage and Low Voltage becomes a larger problem 1], and new Demand Response (DR) programs are emerging. Economic optimization under dynamic pricing, as well as provision of ancillary services and regulation through DR has been in the spotlight of Smart Grid research [2,3]. Balancing demand and supply has predominantly been achieved by supply regulation, but in some cases by demand regulation too. Any regulation measure from the “supply side” has an equivalent. A. Kleidaras et al / Energy 145 (2018) 754e769
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