The Combined Economic and Emission Dispatch (CEED) plays a crucial role in balancing cost-effective electricity generation from both traditional and renewable energy sources with environmental considerations. Several existing solutions attempt to solve this problem. 'Black-box' models, while adept at processing large datasets, often fall short in delivering optimal solutions. Conversely, 'white-box' models, despite their theoretical precision, grapple with uncertainties and typically exhibit slower performance. To address these limitations, this paper introduces a 'gray-box' model framework. Our study incorporates a scenario featuring four Thermal Units (TUs) and six Solar Units (SUs). Results indicate that the introduced hybrid algorithms capture over 90% of the optimal behavior, signifying a notable stride in addressing energy optimization challenges. Their swift execution times particularly stand out, making them highly suitable for real-time operational scenarios.
