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2019 | OriginalPaper | Buchkapitel

2. Modeling Demand Response Resources

verfasst von : Pengwei Du, Ning Lu, Haiwang Zhong

Erschienen in: Demand Response in Smart Grids

Verlag: Springer International Publishing

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Abstract

In general, end use loads are divided into four categories: residential, commercial, industrial and agricultural. In this chapter, our interest is in modeling residential and commercial building demand response (DR) resources. Unlike generators, end use electricity consumptions are heavily influenced by customer preferences. So, the human factors need to be modeled besides the working principles of DR resources. We will focus on modeling steady-state behaviors of a DR resource so the transient response is ignored.
In this chapter, we will introduce the modeling of thermostatically controlled loads (TCLs), non-TCL controllable loads, and the base load. Electricity consumptions of an ideal DR resource can be curtailed or shifted without causing enduring inconvenience and severe degradation in performance and lifetime. Therefore, TCLs, such as heat ventilation and air conditioning (HVAC) units, water heaters, and refrigeration loads, are generally considered to be the most suitable DR resources. Non-TCL, infrequently used large loads such as washers and dryers, exterior lighting, and pool pumps, are also good DR resources. Must-run loads, such as interior lighting, entertainment, and cooking loads, are usually considered as the base load because their consumptions can be neither delayed nor reduced.

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Fußnoten
1
Demand charges are designed to reduce the customer consumption during peak load periods. For example, the highest 15-minute power consumption of a month will be recorded and be charged at a much higher $/kW rate than then energy charges.
 
Literatur
1.
Zurück zum Zitat Lu, N. (Sept. 2012). An evaluation of the HVAC load potential for providing load balancing service. IEEE Transactions on Smart Grid, 3(3), 1263–1270.CrossRef Lu, N. (Sept. 2012). An evaluation of the HVAC load potential for providing load balancing service. IEEE Transactions on Smart Grid, 3(3), 1263–1270.CrossRef
2.
Zurück zum Zitat Du, P., & Lu, N. (2011). Appliance commitment for household load scheduling. IEEE Transactions on Smart Grid, 2(2), 411.CrossRef Du, P., & Lu, N. (2011). Appliance commitment for household load scheduling. IEEE Transactions on Smart Grid, 2(2), 411.CrossRef
3.
Zurück zum Zitat Kondoh, J., Lu, N., & Hammerstrom, D. J. (2011). An evaluation of the water heater load potential for providing regulation service. IEEE Transactions on Power Apparatus and Systems, 26(3), 1309–1316.CrossRef Kondoh, J., Lu, N., & Hammerstrom, D. J. (2011). An evaluation of the water heater load potential for providing regulation service. IEEE Transactions on Power Apparatus and Systems, 26(3), 1309–1316.CrossRef
4.
Zurück zum Zitat Vanouni, M., & Lu, N. (2015). Improving the centralized control of thermostatically controlled appliances by obtaining the right information. IEEE Transactions on Smart Grid, 6(2), 946.CrossRef Vanouni, M., & Lu, N. (2015). Improving the centralized control of thermostatically controlled appliances by obtaining the right information. IEEE Transactions on Smart Grid, 6(2), 946.CrossRef
5.
Zurück zum Zitat Vanouni, M., & Lu, N. (2018). A reward allocation mechanism for thermostatically controlled loads participating in intra-hour ancillary services. IEEE Transactions on Smart Grid, 9(5), 4209–4219.CrossRef Vanouni, M., & Lu, N. (2018). A reward allocation mechanism for thermostatically controlled loads participating in intra-hour ancillary services. IEEE Transactions on Smart Grid, 9(5), 4209–4219.CrossRef
Metadaten
Titel
Modeling Demand Response Resources
verfasst von
Pengwei Du
Ning Lu
Haiwang Zhong
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-19769-8_2