A New Algorithm for Multi‐Objective Hybrid Economic Emission Dispatch Using Data‐Driven Forecasting of Wind and Solar Power - Khan, Muhammad Kamran; Khan, Muhammad Ammar; Kauhaniemi, Kimmo; Zafar, Muhammad Hamza;; Rashid, Saad (Institution of engineering and technology (IET), 2025)
This study introduces a forecasting-driven framework for solving the hybrid dynamic economic emission dispatch (HDEED) problem with uncertain wind and solar generation. Unlike conventional approaches that rely on probabilistic models, this research uses data-driven forecasting techniques to predict the output powers of wind and PV plants and uses them in the multi-objective optimisation algorithm to minimise costs and emissions. An arithmetic optimiser with a sine cosine assisted driving training-based optimisation algorithm (AOASC-DTBO) is presented to solve various single and multi-objective HDEED problems based on fuzzy decision-making and the Pareto dominance concept. The proposed dispatching algorithm is validated using 10-unit, 40-unitand 7-unit IEEE 57 bus systems. The results revealed that the AOASC-DTBO algorithm achieved 8.73% and 4.95% lower fuel costs when compared with the PSO and DTBO algorithms, respectively. In addition, the emissions were 6.47% and 2.25% lower than the BMO and DTBO algorithms, respectively. The work highlights the importance of integrating renewable energy sources (RES) into power systems to achieve cost savings and reduced emissions, while also emphasising the need for efficient dispatch algorithms to ensure grid stability and reliability. The results demonstrate that integrating RES into the power system can yield substantial economic and environmental benefits, achieving cost savings of up to $6,908.034 and reducing emissions by 13,233.691 tonnes per day.
Keywords: evolutionary computation; neural nets; transmission planning and operation; fi=Sähkötekniikka|en=Electrical Engineering|