Robotic Expert System for Energy Management in Distributed Grid Ecosystem

Robotic Expert System for Energy Management in Distributed Grid Ecosystem

Ononiwu Gordon Chiagozie (Federal University of Technology Owerri, Owerri, Nigeria), Kennedy Chinedu Okafor (Federal University of Technology, Owerri, Nigeria) and Nwaokolo F I (Federal University of Technology Owerri, Owerri, Nigeria)
Copyright: © 2020 |Pages: 26
DOI: 10.4018/IJEOE.2020010101
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A robotic expert system (RES) for energy management (EM) in community-based micro-grids is developed using a fuzzy computational scheme. Within the micro-grid multi-dimensional space, embedded algorithms for residential homes, sectors and central controller units are introduced to perform EM in a collaborative manner. Demand response and load shedding are carried out within the community micro-grid to ascertain the behavioral responses based on changes in power demand levels. Various tests are carried out with an observable low error margin. It was observed that the system reduced the total power demand on the micro-grid by 20% of the total distributed power. Micro-grid RES, neuro-fuzzy control (NFC), and support vector regression (SVR) evaluations are compared considering the home units at 40kW of the generated capacity. The results gave a 35.79%, 31.58% and 32.63% energy demand, respectively. Consequently, RES provides a grid look-ahead prediction, annotated-self healing, and stability restoration.
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1. Introduction

1.1. Background of Study

The traditional centralized form of energy generation in Africa has been found to be inadequate to meet today’s energy demands. In locations that have sufficient energy, it usually appears erratic and thus unreliable. Again, certain communities are located far away from the utility grid. This makes their connection to the grid rather expensive considering the cost of transmission equipment. These factors have inspired energy consumers within the region to generate their electricity. The consequence is found in enormous energy wastages as most individuals generate more or less than their energy demands. There is no framework/design for sharing energy among the several consumers since the present configuration does not allow for scalability. This necessitates a shift towards distributed generation (DG) which involves several micro-grids isolated or non-isolated from the utility grid in form of Smart-grid (Makdisie, Badia, & Alhelou, 2018). DG comes with several issues like power quality (from the several generating stations), energy management/control within the micro-grid (balancing energy demand and supply), security, smart metering for tariff management. For instance, efforts on smart metering were discussed by (Tonyali, Akkaya, Saputro, Uluagac, & Nojoumian, 2018), focusing on privacy-preserving protocols for secure and reliable data aggregation in Internet of Things (IoT)-enabled smart metering systems. (Manzoor, Javaid, Ullah, Abdul, Almogren, & Alamri, 2017) focused on an intelligent hybrid heuristic scheme for smart metering based on demand side management in smart homes. Non-intrusive load monitoring in power grids has been proposed by (Chui, Lytras, & Visvizi, 2018) and (Tabatabaei, Dick, & Xu, 2017). Most solutions are found in Cyber-physical systems (Okafor, 2019)

The challenge is to develop an architectural model for managing energy resources for the community based micro-grid system. This involves sharing energy among members of an ‘energy community’. For instance, the grid-networks by (Alhelou, Hamedani, & Askari-Marnani, 2018) offered the use of robust sensors for fault detection as well as isolation in modern smart power systems. Such future smart micro-grids (Alhelou, Golshan, & Masoud, 2015; Alhelou, & Golshan, 2016) could explore multi-agent controls in energy systems. However, achieving a lower computational EMS requires the use of an intelligent scheme for system efficiency.

In this paper, an automated system (RES) is proposed to optimally coordinate the several aspects of the community based micro-grid network. This provides proper operation and reliability for sub-Saharan African communities. An important aspect of the problem is to find an optimal configuration for sharing energy resources among members of the proposed community. The proposed system supports grid annotated-self healing and stability restoration in a production environment.

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