Kurte, K.R.Raju, M.M.Dongritot, P.Kulkarni, K.2026-02-0620232023 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy: Power Electronics, Smart Grid, and Renewable Energy for Sustainable Development, PESGRE 2023, 2023, Vol., , p. -https://doi.org/10.1109/PESGRE58662.2023.10404595https://idr.nitk.ac.in/handle/123456789/29313To mitigate the impact of climate change, industries worldwide are adopting renewable energy to meet power demand. However, optimizing power generation costs and reducing emissions by integrating renewables with conventional sources presents significant challenges. In this work, we present a multi-objective optimization approach to dynamically optimize both costs and emissions by accounting for fossil-fuel based generators, renewables such as wind and solar, as well as the uncertainty in the demand and renewable energy forecasts. The primary objective of this work is to demonstrate how a multi-objective optimization framework can be used to generate reliable recommendations for emission reduction with minimal impact on cost. Our study reveals that significant emission reduction (≈ 50%) is achievable with a slight increase in the cost budget (≈ 2%). © 2023 IEEE.dynamicemission reductionmulti-objectiveOptimal power dispatchrenewable energyuncertainty