To effectively model and solve power systems, you need the right process and tools.

If harnessed correctly, the wind could power the world 11 times over every year. But it’s a big challenge for power engineers to plan and operate wind farms that can harness energy effectively.

Optimization can help. In our recent webinar, Dr. Alireza Soroudi, Assistant Professor at the University College Dublin, walked through how to use optimization in several of today’s most pressing power scenarios, from planning a wind farm in Ireland to integrating gas and electrical grids. Here are some of the highlights of what he covered.

What is optimization?

In a nutshell, optimization helps you make the best choices about the resources you have. You can think of it like this: You have a bit of money in your piggy bank, and you need to decide what you’re going to do with it. Are you going to spend it all on a 1-week cruise to Hawaii? Or are you going to spread it out over a year and buy books and coffee?

When it comes to power systems, optimization is about minimizing your costs and maximizing the efficiency and security of the system. You need to figure out how to generate enough power to meet demand, while spending as little money as possible.

For example, let’s say you want to set up a wind farm off the coast of Ireland. Good choice, as Ireland boasts nearly 300 wind farms that generate up to 30% of the country’s electricity, depending on the time of year. In this scenario, you’d need to account for many constraints, such as:

• Wake effect: If wind turbines are too close to each other, they cancel out the amount of energy they can absorb.
• Physical distance: It can be more costly to run and maintain turbines that are farther apart from each other and from land.
• Network capacity: Your network might not be ready to absorb the amount of wind that can be generated in the area.
• Public acceptance: People these days often want clean energy, but they can be less keen on installing wind turbines that clutter their ocean view.

Approaching the problem

As you can see, there are often significant complexity and constraints to these sorts of power problems. But the way to approach them is simple:

1. Start with an accurate model. As the decision-maker or coding expert, you need to thoroughly understand the problem you’re trying to solve, the constraints, and any input data. You also need to understand your goals, such as to minimize or maximize cost or input.
2. Translate to code. Next, you translate your problem into code, into a language that a solver can understand. The reason being, solvers aren’t magicians—they can’t parse mechanical equations, chemical reactions, or public sentiment. You should plan to spend a bulk of your time on this piece (even around 95%), as you need to make a model that accurately represents the problem you’re trying to solve.
3. Solve it. You then feed your model into a solver, which lets you process your data at a reasonable speed and even share it with a visualizer. If you’ve taken the time to model your problem correctly, then the solver makes up only that last 5% of effort, tweaking things here and there so you get the result you need.

Watch the webinar

To effectively model and solve power systems, you need the right process and tools. To learn more about what this looks like across various scenarios—from green hydrogen to uncertainty modeling—watch the webinar on-demand: Modern Power System Operation and Planning Under the Optimization Lens.

Also, just a reminder that Gurobi is always free for academics—and it’s available to recent grads through our Take Gurobi With You Program. For more, check out our Academic Program or email academicprogram@gurobi.com.

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