A Multi-Objective Optimization Dynamics for Sustainable Smart Grid Design of Engineering Disciplines
Year: 2025
Type: conference-paper
Abstract: With avalanche of smart technological development on the horizon, one is of the view that the availability of smart technologies increases the demand for reliable, cost-effective and environmentally sustainable energy supplier system in place. To have such system in place, this study recognizes the need that necessitates a multi-disciplinary approach for the design and optimization of modern power grids. Thus, this study derives, an integrated mathematical optimization model for the development of a sustainable smart grid system that engages the interests of electrical, mechanical, metallurgical, civil, and control engineering disciplines. The engagement of such engineers has the potential to minimizes the total cost of energy generation, distribution, and infrastructure; while sustaining the environmental and maximizing energy eficiency, reliability. Therefore, to achieve our main objectives, an integrated mathematical optimization model incorporating power generation, energy demand, reliability, eficiency, maintenance costs, material lifespan, and emissions, subject to a set of constraints that ensure system balance, capacity limits, minimum reliability, eficiency, and compliance with environmental regulations. It is our strong hope that by optimizing these variables, the integrated mathematical optimization model addresses the critical challenges faced by various engineering fields. Since, electrical engineering focuses on the eficient distribution and reliability of energy; mechanical engineering on the performance and longevity of turbines and power systems; metallurgical engineering on material durability and eficiency; and civil engineering on the infrastructure required to support the grid; and furthermore, control engineering contributes automated solutions for load balancing and the integration of renewable energy sources. Thus, optimization model becomes a multi-objective optimization framework that provides a comprehensive solution.
Source: Engineering headway
DOI: 10.4028/p-gvd5vt
Authors: Albert Shikongo, Lunga M. Matsebula
Institution: University of Namibia
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415812964