Enhancing dynamic economic emission dispatch through renewable energy prediction and multi-objective optimisation

Abstract

This study proposes an integrated framework for renewable-energy-assisted Dynamic Economic Emission Dispatch to support the transition toward net-zero power system operation. The framework uniquely combines multi-output renewable energy forecasting with advanced multi-objective optimisation to enhance dispatch efficiency and reliability. A unified feedforward Artificial Neural Network is developed and trained offline using 9 years (2009–2018) of NASA MERRA-2 meteorological data for the Texas region (31.0°N, −100.0°W) to simultaneously predict hourly solar irradiance and wind speed. The predicted values are converted into renewable power and directly incorporated into the DEED formulation. In addition, a secondary ANN-based model is employed to estimate transmission losses, eliminating the need for repeated load-flow calculations and overcoming the limitations of conventional B-loss coefficients. The resulting bi-objective optimisation problem, which minimises fuel cost and emissions, is solved using the Multi-Objective Grey Wolf Optimiser, enabling the generation of a diverse Pareto front in a single run. A fuzzy membership approach is then applied to identify the best compromise solution. The proposed framework is validated on a modified IEEE 30-bus system under multiple scenarios, including solar-only, wind-only, and hybrid renewable integration. Results indicate that hybrid integration achieves a maximum daily fuel cost saving of $540.24 and an emission reduction of 0.19 tons compared to the thermal-only case. These findings demonstrate that the proposed ANN–MOGWO framework provides a scalable and computationally efficient solution for dispatch planning in renewable-rich power systems, thereby reducing operational costs and enhancing environmental performance.