A Novel Solution for Day-Ahead Scheduling Problems Using the IoT-Based Bald Eagle Search Optimization Algorithm

Abstract

Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Additionally, due to environmental, technical and economic constraints, it is challenging to meet demand at certain hours, such as peak hours. Therefore, it is necessary to manage network consumption to modify the peak load and tackle power system constraints. One way to achieve this goal is to use a demand response program. The home energy management system (HEMS), based on advanced internet of things (IoT) technology, has attracted the special attention of engineers in the smart grid (SG) field and has the tasks of demand-side management (DSM) and helping to control equality between demand and electricity supply. The main performance of the HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) to discover the optimal scheduling of home appliances. Furthermore, the HEMS architecture is programmed based on MATLAB and ThingSpeak modules. The HEMS uses the BESOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the PAR, and increase user comfort. The results show the suggested system’s ability to obtain optimal home energy management, decreasing the energy cost, microgrid emission cost, and PAR (peak to average ratio).

Review of Smart Transformer-Based Meshed Hybrid Microgrids: Shaping, Topology and Energy Management Systems

“…In [92], a real-time dispatch architecture based on the Gray Wolf Optimizer (GWO) and Artificial Bee Colony optimization (ABC) algorithm is proposed [92], in which a Time-of-Use (ToU)-based pricing model defines the peak hour rates optimally. Likewise, [93] presents a multiobjective version of a metaheuristic algorithm called the Bald Eagle Search Optimization Algorithm (BESOA) for discovering the optimal scheduling of home appliances at the Smart Home (SH) system level. Under this SH perspective, [94] develops a real-time DER residential load control method based on an input and optimization algorithm to control and schedule loads and Peak-To-Average Rate (PAR) savings.…”

A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems

“…Incentive-Based Demand Response Programs [51] The IoT-based bald eagle search optimization algorithm was used by the authors to suggest solutions for day-ahead scheduling issues.…”

Integrated Demand Response Optimization for Consumer With Herd Mentality: A Genetic Simulated Annealing Approach

“…[23] develops an Ant Colony Optimization (ACO) algorithm to minimize PAR, energy costs, and carbon emissions by balancing demand and power generation. In [24], the authors propose a multiobjective version of the bald eagle search optimization algorithm to find the optimal scheduling modes, aiming to reduce energy costs, microgrid emission costs, and PAR. The residential load is scheduled with the goal of minimizing the PAR and electricity consumption cost contemplating the time-of-use electricity price (ToU) in [25].…”