Multi-objective-based economic and emission dispatch with integration of wind energy sources using different optimization algorithms

In this work, a study of economic and emission dispatch issues based on the multi-objective optimization is solved, and generation costs and emissions are reduced by utilizing multi-objective optimization techniques. This optimization is carried out in an IEEE-30 bus system, with and without the integration of wind energy sources, with equality and inequality constraints. The equality constraints are the power balance constraints, stipulating that to have an optimal solution, the generated power must be adequate to satisfy the load demand plus losses. The inequality constraints are a collection of limitations for active power generation, reactive power generation, generator bus voltage, and load bus voltage. To track the hourly load demand, a daily load profile is established using the IEEE-30 bus system. The generation costs and emissions in the system are optimized using multi-objective particle swarm optimization and multi-objective Ant–Lion Optimization approaches. In order to determine the goals’ minimum values, a fuzzy min–max technique is applied. The values that have been minimized are then compared to determine how well wind energy integration has reduced the generation costs and emissions. Two case studies are performed in this work. For Case 1, the total generation costs and emissions using MOPSO are less, with a difference of $42.763, while MOALO has lower emissions, with a difference of 157.337 tons. For Case 2, with the implementation of wind energy, MOPSO has lower total generation costs, with a difference of $51.678, and lower emissions, with a difference of 459.446 tons.