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A Privacy-Preserving Distributed Greedy Framework to Desynchronize Power Consumption in a Network of Thermostatically Controlled Loads
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1364-8127
University of Cagliari, Diee, Cagliari, Italy.
University of Cagliari, Diee, Cagliari, Italy.
2024 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 32, no 6, p. 2476-2483Article in journal (Refereed) Published
Abstract [en]

This manuscript presents a novel distributed greedy framework applicable to a network of thermostatically controlled loads (TCLs) to desynchronize the network's aggregated power consumption. Compared to the existing literature, our proposed framework offers two distinct novelties. First, our proposed algorithm relaxes the restrictive assumptions associated with the communication graph among TCLs. To elaborate, our algorithm only requires a connected graph to execute control, a condition less demanding than its counterpart algorithms that mandate a star architecture, K-regular graphs, or undirected connected graphs. Second, a significant novel feature is the relaxation of the obligation to share private information, such as each unit's local power consumption and appliance temperatures, either with a central coordinator or neighboring TCLs. The findings presented in this brief are validated through simulations conducted over a network comprising 1000 TCLs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2024. Vol. 32, no 6, p. 2476-2483
Keywords [en]
Demand response, distributed optimization, greedy control, multiagent systems, thermostatically controlled loads (TCLs), Differential privacy, Undirected graphs, Communication graphs, Connected graph, Multi agent, Power, Privacy preserving, Thermostatically controled load, Thermostatically controlled loads
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-68822DOI: 10.1109/TCST.2024.3425210ISI: 001273023000001Scopus ID: 2-s2.0-85207417269OAI: oai:DiVA.org:mdh-68822DiVA, id: diva2:1910904
Available from: 2024-11-06 Created: 2024-11-06 Last updated: 2025-04-07Bibliographically approved

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Kaheni, KaheniPapadopoulos, Alessandro

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