In the world of engineering, tackling multiple goals at once is a common headache. When it comes to running combined heat and power systems, the struggle is real. The aim is to keep costs low while also cutting down on pollution. This is where smart algorithms come into play.

One such algorithm is the multi-objective differential evolution algorithm. It has been given a makeover to handle these competing goals better. This upgrade includes tweaks like adaptive scaling and crossover, non-dominated sorting, and even adding elite populations and special mutation techniques. The goal? To create a dynamic model that optimizes both economic and environmental benefits.

So, how does this improved algorithm fare? In tests, it showed impressive results. It managed to balance the trade-off between cost and pollution effectively. When put to the test on a real-world system, it proved its worth. The system had two cogeneration units, seven generator units, and one heating unit. The improved algorithm slashed fuel costs to as low as $2, 300, 590 and reduced pollution emissions to 200, 285 kg. This is a significant achievement, showing that it can handle the complexities of real-world applications.

The improved algorithm also performed better in various metrics. It had a better Pareto optimal frontier distribution, which means it found a good balance between cost and pollution. It also excelled in hyper volume and inverted generational distance metrics, indicating its strong optimization capabilities.

But why does all this matter? Well, in practical terms, it means more efficient systems. It means lower operational costs and reduced pollution. It means a win-win situation for both the economy and the environment. This algorithm shows promise in making power systems more sustainable and cost-effective.

However, it's important to note that while this algorithm shows great potential, it's not a one-size-fits-all solution. Different systems may require different approaches. But it's a step in the right direction, showing that with the right tools, we can balance cost and pollution in power systems.

MULTI OBJECTIVE OPTIMIZATION ALGORITHMS

Balancing Cost and Pollution in Power Systems

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