A Greedy Adaptive and Backtracking Framework for Reducing Emission Costs in Generator Scheduling

The growing demand for efficient and environmentally sustainable power generation calls for advanced optimization methods to address the economic and emission dispatch (EED) problem. This study introduces a novel hybrid optimization approach, GRASP-BLS, which integrates GRASP for global solution space exploration with BLS for accurate local refinement. The synergy between metaheuristic randomness and gradient-based precision is the main contribution of this work, enabling GRASP-BLS to outperform conventional methods in complex, constrained power dispatch scenarios. In a 4-h test case, GRASP-BLS reduced generation costs by 0.4% and emissions by 22.25% compared with SEA. Extended evaluations over a 24-hour period under two scenarios—Scenario 1 (stable loads) and Scenario 2 (dynamic loads with unit commitment)—show that GRASP-BLS consistently yields superior performance. It achieves 5.64%–9.84% cost savings and 1.72%–4.91% emission reductions, outperforming GSA, GWO, and PSO. Despite slightly higher computation time, GRASP-BLS satisfies all operational constraints, including power balance, generation limits, ramp rates, and unit commitment feasibility. These findings highlight the novelty and practicality of GRASP-BLS as a robust, scalable, and adaptive framework for real-world power system optimization, particularly in environments requiring a balance between economic efficiency and environmental responsibility.

Backtracking Line Search; Economic Emission Dispatch; GRASP; Optimization; Power System

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