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A Direct Load Control Model for Virtual Power Plant Management

Ruiz, N., Cobelo, I., Oyarzabal, J.·2009·IEEE Transactions on Power Systems

Abstract

In the framework of liberalized electricity markets, distributed generation and controllable demand have the opportunity to participate in the real-time operation of transmission and distribution networks. This may be done by using the virtual power plant (VPP) concept, which consists of aggregating the capacity of many distributed energy resources (DER) in order to make them more accessible and manageable across energy markets. This paper provides an optimization algorithm to manage a VPP composed of a large number of customers with thermostatically controlled appliances. The algorithm, based on a direct load control (DLC), determines the optimal control schedules that an aggregator should apply to the controllable devices of the VPP in order to optimize load reduction over a specified control period. The results define the load reduction bid that the aggregator can present in the electricity market, thus helping to minimize network congestion and deviations between generation and demand. The proposed model, which is valid for both transmission and distribution networks, is tested on a real power system to demonstrate its applicability.

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Metadata

Authors
Ruiz, N., Cobelo, I., & Oyarzabal, J.
Venue
IEEE Transactions on Power Systems
Published
2009-05
Volume
24
Issue
2
Pages
959-966
ISSN
  • 0885-8950 (ISSN-L)
  • 1558-0679
OpenAlex
W2120885393
Cited by
610
References
0
FWCI
17.16
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Direct Load Control Model for Virtual Power Plant Management. https://app.cochat.ai/papers/10.1109/tpwrs.2009.2016607
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Ruiz, N., Cobelo, I., & Oyarzabal, J. (2009). A Direct Load Control Model for Virtual Power Plant Management. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2009.2016607

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.