Cooperative Demand Response Framework for a Smart Community Targeting Renewables: Testbed Implementation and Performance Evaluation

Carlos Cruz, Esther Palomar, Ignacio Bravo and Alfredo Gardel
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Carlos Cruz: Department of Electronics, University of Alcala, Alcala de Henares, 28805 Madrid, Spain
Esther Palomar: Department of Electronics, University of Alcala, Alcala de Henares, 28805 Madrid, Spain
Ignacio Bravo: Department of Electronics, University of Alcala, Alcala de Henares, 28805 Madrid, Spain
Alfredo Gardel: Department of Electronics, University of Alcala, Alcala de Henares, 28805 Madrid, Spain

Energies, 2020, vol. 13, issue 11, 1-20

Abstract: Demand response (DR) is emerging as the workhorse of achieving energy efficiency and reducing our carbon footprint, which persists as a major challenge amongst all the different energy-chain players, i.e., the utility providers, policy makers, consumers, and the technology sector. For instance, the Internet-of-Things (IoT) paradigm and network-enabled appliances/devices have escalated the expectations of what technology could do for the acceptance of DR programs. In this work, we design, deploy on a scalable pilot testbed, and evaluate a collaboration-based approach to the demand-side management of a community of electricity consumers that jointly targets green consumption. The design of the framework architecture is centralized via the so-called aggregator , which optimizes the demand scheduled by consumers along with their time frame preferences towards the maximization of the consumption of renewables. On the pilot, we opt for lightweight, yet efficient platforms such as Raspberry Pi boards, and evaluate them over a series of network protocols, i.e., MQTT-TLS and CoAP-DTLS, paying special attention to the security and privacy of the communications over Z-Wave, ZigBee, and WiFi. The experiments conducted are configured using two active Living Labs datasets from which we extract three community scenarios that vary according to the flexibility or rigidity of the appliances’ operation time frame demand. During the performance evaluation, processing and communication overheads lie within feasible ranges, i.e., the aggregator requires less than 2 s to schedule a small consumer community with four appliances, whereas the latency of its link to households’ controllers adds less than 100 ms. In addition, we demonstrate that our implementations running over WiFi links and UDP sockets on Raspberry Pi 4 boards are fast, though insecure. By contrast, secure CoAP (with DTLS) offers data encryption, automatic key management, and integrity protection, as well as authentication with acceptable overheads.

Keywords: cooperative demand response; consumption scheduling; renewable supply; Raspberry Pi board; performance evaluation; CoAP; MQTT; TLS/DTLS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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