Artificial intelligence (AI) is about to change all that. As design is essentially an evolution of solutions, AI techniques such as genetic algorithms (GA) are an excellent fit to the nature of many design problems and can generate a rich variety of competitive designs in the same way genetics does for biology (no two leaves are the same but they both work). These powerful tools have the potential to help people learn, design, and discover new things. In this article, I demonstrate how GA can be used to design a photovoltaic (PV) solar farm. As always, I first provide a short screencast video in which I used the daily output or profit as the objective function to speed up the animation so that you can see the evolution driven by GA. The actual assessments are based on using the annual output or profit as the objective function, presented in the text that follows the video. Note that the design process is still geared towards a single objective (i.e., the total output in kWh or the total profit in dollars over a given period of time). Design problems with multiple objectives will be covered later.
In GA, the solution depends largely on the choice of the objective function (or the fitness function), which specifies the main goal. For example, if the main goal is to generate as much electricity as possible on a given piece of land without the concern of the cost of solar panels, a design in which solar panels are closely packed may be a good choice. On the other hand, if the main goal is to generate as much electricity as possible from each solar panel because of their high price, a design in which rows of solar panels are far away from one another would be a good choice (in the case shown in the video, a single rack of solar panels was unsurprisingly found as the best solution). The real-world problems always lie between these two extremes, which is why they must be solved using the principles of engineering design. The video above clearly illustrates the design evolution driven by GA in the three cases (the two extremes and an intermediate).
|Figure 1. An Energy3D model of an existing solar farm in Massachusetts.|
|Figure 2. The results of 10 iterations.|
For the objective function, we can select the total annual output, the average annual output of a solar panel, or the annual profit. I chose the annual profit and assumed that the generated electricity would sell for 22.5 cents per kWh (the 2018 average retail price in Massachusetts) and the daily cost of a solar panel (summing up the cost of maintenance, financing, etc.) would be 20 cents. I don't know how accurate these ROI numbers are. But let's just go with them for now. The annual profit is the total sale income minus the total operational cost. Qualitatively, we know that a higher electricity price and a lower operational cost would both favor using more solar panels and a lower electricity price and a higher operational cost would both favor using less solar panels. Finding the sweet spots in the middle requires quantitative analyses and comparisons of many different cases, which can be outsourced to AI.
|Figure 3: The best design from 2,000 solutions|
|Figure 4: The second best design from 2,000 solutions.|
The best solution (Figure 3) that emerged from these 2,000 generated solutions used 5,420 solar panels fixed at a tilt angle of 28.3° to generate 2,667 MWh per year and was about 16% better than the existing one based on the ROI model described above. The second best solution (Figure 4) used 4,670 solar panels fixed at a tilt angle of 38.6° to generate 2,340 MWh per year and was about 5.5% better than the existing one based on the ROI model. Note that if we use the average annual output per solar panel as the criterion, the second best solution would actually be better than the best one, but we know that the average panel output is not a good choice for the fitness function as it can result in an optimal solution with very few solar panels.
In conclusion, the generative design tools in Energy3D powered by AI can be used to search a large volume of the solution space and find a number of different solutions for the designer to pick and choose. The ability of AI to transcend human limitations in complex design is a significant application of AI and cannot be more exciting! We predict that future work will rely more and more on this power and today's students should be ready for the big time.