Multi-Objective Mathematical Optimization Framework for Renewable Energy Systems

Abstract

The growing inclusion of the renewable energy systems (RES) into existing power networks are extremely challenging due to the stochastic nature of their production, uncertainty in their functioning, and a mixture of the economic, environmental, and technical performance needs to be taken into account. In this paper, I have introduced a multi-objective mathematical programming model that would enhance planning and management of the hybrid renewable systems. The primary aim is to unite the reduction cost, emission reduction and improvement of reliability within a single optimization model. The model employs Mixed-Integer Linear Programming (MILP) in performing the structured decision-making task, Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and hybrid metaheuristic to find Pareto-optimal trade-offs successfully. The case study of a hybrid solar-wind-battery micro-grid is conducted with a view to evaluating the functionality of the framework in reality resource and demand scenarios. The approach suggested has been demonstrated by simulation to reduce the Levelized Cost of Energy (LCOE) by 18 percent, increase the solar and wind penetration by 42 percent, and boost system reliability by 66 percent, measured by the Loss of Load Probability (LOLP). The decision-makers can make use of the Pareto frontier generated to determine trade-offs in economic, environmental, and reliability objectives; this offers an effective policy-making and investment planning instrument. The research will be used to create methods of optimizing sustainable energy using a framework that is scalable and flexible in order to expand to microgrids, networks of regions and integration of renewable energy nationwide. The approach will be extended to real time smart grid applications involving uncertainty modeling and demand side control in future research.