Towards Energy Efficient Smart Grids Using Bio-Inspired Scheduling Techniques

Abstract

Electric power grids are lagging in flexibility and time-response. A smart grid is an improved version of electrical grids that leverages Internet of Things (IoT) based devices to improve the overall infrastructure from the grid stations to intelligent appliances. It provides better understanding of supply and demand and overall flow of data depending based upon the requirements. Modern approach towards Smart grid envisions to provide electricity consumers with the opportunity to manage their respective power usage. Population increase has played a major role in the adoption of smart grid as a lot of electrical energy is consumed in the residential sector and a lot of architectures have been proposed for better flow of information from the smart meter to connectors and devices for improved customer participation. Customer needs have been very important in the smart grid. However, the customers have never been provided with the ease of choosing their own kind of benefits from the smart grid. In this work, we propose an enhanced architecture working effectively for multiple users based on their requirements. The users would be able to choose their type of scheduling techniques based on their requirements. These requirements may include cost reduction and increasing user comfort for better consumption of electricity and reliable systems. These requirements can be achieved using different Bio inspired computing based scheduling algorithms. Furthermore, in this work, we provide a comparison of these bio inspired scheduling techniques, i.e., Enhanced Differential Evolution, Bacterial Foraging Algorithm and Grey Wolf Optimization integrated in smart grid architecture for providing better consumption of electricity and achieving reliable systems. These algorithms mainly aim to schedule load, minimize electricity bills and maximize the user comfort depending on user demand.

Nature-Inspired Algorithms for Energy Management Systems

“…The improvements in various parameters using different algorithms in Energy management system are shown in Fig. 4 (Amjad et al, 2020;Rahim et al, 2016;Imran et al, 2020;Awais et al, 2018)…”

A Strategy for Multi-Objective Energy Optimization in Smart Grid Considering Renewable Energy and Batteries Energy Storage System

“…Dynamic model using SCADA is developed for BESS charging/discharging scheduling to minimize power loss and economic factors due to power exchange between SPG and microgrid in [63]. Bacteria foraging and grey wolf optimization algorithms are adopted for a utility bill and discomfort minimization in [64]. A multi-objective dragonfly algorithm schedules PVs/PHEVs charging/discharging and load using DR in [65].…”

A Trust-Based Framework and Deep Learning-Based Attack Detection for Smart Grid Home Area Network

“…This could lead to people depriving themselves of adequate heating or lights. [7] proposed an improved framework operating efficiently for several users depending on their necessities. Depending on their needs, the users can select the kind of scheduling method.…”