Efficient Power Sharing at the Edge by Building a Tangible Micro-Grid – the Texas Case

Abstract

Information and Communication Technology (ICT) is now touching various aspects of our lives. The electricity grid with the help of ICT is transformed into Smart Grid (SG) which is highly efficient and responsive. It promotes twoway energy and information flow between energy distributors and consumers. Many consumers are becoming prosumers by also producing energy. The trend is to form small communities of consumers and prosumers leading to Micro-grids (MG) to manage energy locally. MGs are parts of SG that decentralize the energy flow by allocating the produced energy within the community. Energy allocation amongst them needs to solve issues viz., (i) how to balance supply/demand within micro-grids; (ii) how allocating energy to a user affects his/her community. To address these issues we propose six Energy Allocation Strategies (EASs) for MGs -ranging from simple to optimal. We maximize the usage of the energy generated by prosumers within MG. We form household-groups sharing similar characteristics to apply EASs by analyzing thoroughly energy and socioeconomic data of households. We propose four metrics to evaluate EASs. We test our EASs on the data from 443 households over a year. By prioritizing specific households, we increase the number of fully served households up to 81% compared to random sharing.

Efficient Allocation of Harvested Energy at the Edge by Building a Tangible Micro-Grid—The Texas Case

“…However, apart from the MG models in which the CC is solely a communication point, there are also models in which it connects to the power line of the substation, to store and forward the excess energy from prosumers to (members of) C using the EAS-algorithms (see, right part of Fig. 1) [1], [6]. Since prosumers have their own energy needs, they cannot allocate all their generated energy to consumers.…”

“…To acquire the energy consumption/generation perspective of households over longer periods (e.g., yearly), the metrics of temporal membership and adaptability are used. Cluster membership refers to the presence of a household in one of the clusters that are defined for an energy attribute and cluster adaptability refers to the transition between different clusters of the same attribute in consecutive time intervals (clustering periods) [1], [6], [9]. In consumption terms, a transition from high to low clusters is considered beneficial because the household saves energy and expenses.…”

“…This work was supported in part by the InSecTT Project through the Electronic Component Systems for European Leadership Joint Undertaking under Grant 876038, and in part by the High-End Foreign Expert Program of China Project under Grant G20200233008. This article was presented in part at 2020 IEEE International Conference on Communications (ICC) [1]. The editor coordinating the review of this article was E. Ayanoglu.…”

“…1) We characterize the MG community regarding the excess and deficiency of energy of its members. 2) We propose three optimal EASs to maximize energy sharing and minimize the energy borrowed from the substation based on game theoretic and information theoretic formulations [1], [6]. 3) We propose three simple EASs for MGs without centralized energy storage.…”