Weighted cost emission dispatch optimization using GA–APO hybridization under priority sensitive scheduling for thermal power systems

Modern utilities operate in an environment where fuel expenditure cannot be viewed in isolation from the environmental impact of generation. This creates a scheduling problem that is harder to address with traditional single objective tools, especially when the fuel and emission characteristics of thermal units do not behave smoothly. In this work, a two-stage solution strategy is developed for the economic-emission dispatch problem. The idea is straightforward: use a Genetic Algorithm (GA) to search widely for feasible production patterns and then pass its best candidate to an Arctic Puffin Optimization (APO) based refinement step, which adjusts the schedule locally and attempts to settle it closer to a desirable operating point. The economic and environmental indices are combined through a weighted formulation so that the dispatch can be steered toward cost saving, emission reduction, or an intermediate compromise without reworking the underlying model. Proposed method is tested on three generators thermal power plant with 24 h scheduling. Under different conditions, the proposed algorithm performed satisfactory by maintaining the results within the operational limits. Comparative study validates the effectiveness of the proposed design over GA approach. In cost-priority operation the hybrid approach achieves up to 1.88% reduction in total operating cost compared to GA. In emission priority condition the proposed GA-APO reduced the emission consumption nearly 0.21% and in balanced case cost per MWh reduced nearly 0.68%.