Modeling uncertainties in aggregated thermostatically controlled loads using a State queueing model

By Ning Lu, David P. Chassin, Steve E. Widergren. IEEE Transactions on Power Systems, 2004

199 citations recorded by the source corpus.

To study the impacts of price responsive demand on the electric power system requires better load models. This paper discusses the modeling of uncertainties in aggregated thermostatically controlled loads using a state queueing (SQ) model. The cycling times of thermostatically controlled appliances (TCAs) vary with the TCA types and sizes, as well as the ambient temperatures. The random consumption of consumers, which shortens or prolongs a specific TCA cycling period, introduces another degree of uncertainty. By modifying the state transition matrix, these random factors can be taken into account in a discrete SQ model. The impacts of considering load diversity in the SQ model while simulating TCA setpoint response are also studied.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/a154c3b6c3eef37fc4912d5a3182358c6d055441

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Citations in the corpus

Benefits and challenges of electrical demand response: A critical review (2014, background): “Both supplier and locational market power can be reduced by allowing demand to respond to locationally differentiated and time varying price, as this limits the ability of larger producers to manipulate the wholesale price of electricity [34–36].”
Optimal scheduling of aggregated thermostatically controlled loads with renewable generation in the intraday electricity market (2017, background): “controlled appliances [16], and further studied the modeling uncertainties using that model [17].”
State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance (2013, result): “This is in contrast to previous work that has either addressed parameter heterogeneity by simulation [15], [16], or assumed homogeneity [17]–[20].”