Power system operators face an increasingly difficult trade-off: minimizing generation costs while reducing air pollutants and greenhouse gas emissions. This dual-objective challenge, known as economic emission dispatch (EED), becomes more complex due to valve-point effects in thermal generators and losses across transmission networks. Traditional optimization methods struggle with the highly nonlinear, non-convex nature of this problem.
Researchers have proposed an improved multi-objective Grey Wolf Optimizer (IMOGWO) to address these competing priorities more effectively. The algorithm employs four key enhancements to improve performance. First, a hybrid initialization combining chaotic mapping and Latin hypercube sampling creates a diverse initial population of candidate solutions. Second, a dream-inspired perturbation mechanism strengthens the algorithm's ability to explore the full solution space globally. Third, a nonlinearly decreasing convergence factor dynamically adjusts how much the algorithm explores new regions versus refining promising solutions. Fourth, incorporation of Lévy flight mechanics helps escape local optima that trap conventional methods.
Testing shows IMOGWO successfully balances the economic-environmental tension, generating solutions that represent genuine trade-offs rather than artificial compromises. The algorithm demonstrates faster convergence, higher solution quality, and greater stability than competing methods. For grid operators, this means dispatch schedules that can better reflect the true costs of generation, including environmental impacts often externalized in traditional approaches.
As renewable energy penetration increases and emissions regulations tighten, such optimization tools become essential infrastructure. They enable operators to maximize grid efficiency while meeting regulatory requirements and corporate sustainability targets. Future applications could integrate this approach with real-time pricing mechanisms and demand response programs, creating more economically efficient and environmentally responsible power systems.
