Ledwaba, Lehlogonolo P.I.Hancke, Gerhard P.Isaac, Sherrin J.Venter, H.S. (Hein)2022-02-222022-02-222021-03-18Ledwaba, L.P.I.; Hancke, G.P.; Isaac, S.J.; Venter, H.S. Smart Microgrid Energy Market: Evaluating Distributed Ledger Technologies for Remote and Constrained Microgrid Deployments. Electronics 2021, 10, 714. https://DOI.org/10.3390/electronics10060714.2079-9292 (online)10.3390/ electronics10060714http://hdl.handle.net/2263/84132This article is an expansion upon the following conference publication: Ledwaba, L.P.I.; Hancke, G.P.; Isaac, S.J.; Venter, H.S. Developing a Secure, Smart Microgrid Energy Market using Distributed Ledger Technologies. In Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland, 22–25 July 2019; pp. 1725–1728, doi:10.1109/INDIN41052.2019.8972018.The increasing strain on ageing generation infrastructure has seen more frequent instances of scheduled and unscheduled blackouts, rising reliability on fossil fuel based energy alternatives and a slow down in efforts towards achieving universal access to electrical energy in South Africa. To try and relieve the burden on the National Grid and still progress electrification activities, the smart microgrid model and secure energy trade paradigm is considered—enabled by the Industrial IoT (IIoT) and distributed ledger technologies (DLTs). Given the high availability requirements of microgrid operations, the limited resources available on IIoT devices and the high processing and energy requirements of DLT operations, this work aims to determine the effect of native DLT algorithms when implemented on IIoT edge devices to assess the suitability of DLTs as a mechanism to establish a secure, energy trading market for the Internet of Energy. Metrics such as the node transaction time, operating temperature, power consumption, processor and memory usage are considered towards determining possible interference on the edge node operation. In addition, the cost and time required for mining operations associated with the DLT-enabled node are determined in an effort to predict the cost to end users—in terms of fees payable and mobile data costs—as well as predicting the microgrid’s growth and potential blockchain network slowdown.en© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.BlockchainDistributed ledger technologyIndustry 4.0Industrial Internet of things (IIoT)Performance testingRaspberry PiSmart microgridSmart contractsSecurityArticle