Abstract
Contrary to the rapid evolution experienced in the last decade of Information and Communication Technologies and particularly the Internet of Things, electric power distribution systems have remained exceptionally steady for a long time. Energy users are no longer passive actors; the prosumer is expected to be the primary agent in the Future Grid. Demand Side Management refers to the management of energy production and consumption at the demand side, and there seems to be an increasing concern about the scalability of Demand Side Management services. The creation of prosumer communities leveraging the Smart Grid to improve energy production and consumption patterns has been proposed in the literature, and several works concerned with scalability of Demand Side Management services group prosumers to improve Demand Side Management services scalability. In our previous work, we coin the term Social Internet of Energy to refer to the integration between devices, prosumers and groups of prosumers via social relationships. In this work, we develop an algorithm to coordinate the different clusters we create using the clustering method by load profile compatibility (instead of similarity). Our objective is to explore the possibilities of the cluster-by-compatibility heuristic we proposed in our previous work. We perform experiments using synthetic and real datasets. Results show that we can obtain a global reduction in Peak-to-Average Ratio with datasets containing up to 200 rosumers and creating up to 6 Prosumer Community Groups, and imply that those Prosumer Community Groups can perform load rescheduling semi-autonomously and in parallel with each other.
Highlights
Contrary to the rapid evolution experienced in the last decade of Information and Communication Technologies (ICTs) and the Internet of Things (IoT) [2], electric power distribution systems have remained exceptionally steady for a long time
This work is a continuation of previous work [8], were we coined the term Social Internet of Energy (SIoE) to refer to the synergy between Demand Side Management (DSM) and Social Internet of Things (SIoT)
We can observe the relation between the increase in the number of clusters and the Peak-to-Average Ratio (PAR) after rescheduling the loads within the same iteration in Figure 6, i.e., for each sample size and iteration: first, we consider within which inter-quartile range is the achieved PAR for k = 1; and, secondly, for each k 6= 1 we look at how many iterations still are within the same inter-quartile group and the consecutive group
Summary
Electric power distribution infrastructures have remained unchanged for a long time, in opposition to the fast evolution of Information and Communication Technologies (ICTs) during the last decade, such as the Internet of Things (IoT) [2]. Traditional and non-renewable energy sources supply most global energy demand; non-renewable energy sources are starting to be insufficient and produce harmful climate changes in our ecosystem [3]. Contrary to the rapid evolution experienced in the last decade of ICTs and the IoT [2], electric power distribution systems have remained exceptionally steady for a long time. Most of the energy demand is met by non-renewable energy resources which are starting to be insufficient and produce undesirable climate changes that harm the world we live in [3].
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