A novel FFA-GWO-based optimization paradigm for solving a dynamic economic emission dispatch in power plants

Thermal power plants are the primary components of the electrical energy production industry. These plants come with certain challenges such as the production of harmful gases causing environmental pollution. This adds to global warming issues that are dangerous for the earth's ecosystem. Another crucial aim is the least cost operation of these power plants. Combining both of these results in the formulation of the Dynamic Economic Emission Dispatch (DEED). This is a multi-objective, constrained, dynamic, complex, and non-linear optimization problem. Traditional methods are insufficient to undertake the complexities of DEED due to challenges such as high computational requirements and local optimal solutions. Hence, for obtaining an optimal or near-optimal solution to such a complex problem, a metaheuristic technique is greatly needed. Metaheuristic techniques offer several benefits to solving complex problems such as being robust, efficient, and overall, less affected by an increase in problem size. In this work, a novel hybrid Firefly Optimization Algorithm (FFA)-Grey Wolf Optimization Algorithm (GWO) has been proposed to solve the DEED problem. The complexity of the DEED problem is undertaken by involving constraints like power limits, power balance, and transmission losses. FFA-GWO was applied on 5, 10, and 15-unit standard test systems to meet 24-h load demands. The optimal costs obtained in 5, 10, and 15-unit standard test systems are 32264$, 1394212.615$, and 465373.448$ respectively. The comparison of results shows that the proposed framework surpasses the other methodologies in terms of reduced fuel cost and emissions.