Dynamic Emission Dispatch using Quadratic Constrained Programming

This paper explores the implementation of Dynamic Emission Dispatch (DED) using Quadratic Constrained Programming (QCP) to minimize emissions from thermal generators while ensuring a reliable power supply in the electrical grid, even under challenging conditions. Traditional dispatch methods often overlook environmental impacts, but DED integrates real-time data, advanced predictive techniques, and adaptive algorithms to dynamically meet stringent emission limits. By utilizing QCP, DED accurately models the complex, non-linear characteristics of power generation and emissions, optimizing the dispatch schedule under varying operational conditions. This adaptability is essential for managing the complexities of power systems, including fluctuating demand, diverse generation sources, and evolving regulatory requirements. This study focuses on the IEEE 30 bus system, comparing QCP with the Lambda Iteration method to demonstrate the practical applicability and effectiveness of the proposed approach. The results indicate that DED with QCP significantly reduces emissions compared to traditional methods like Lambda Iteration, while maintaining grid reliability and supporting environmental sustainability.