Not many people know that several electricity markets in Asia have been modeled and optimized using AIMMS. This includes some of the largest state grids in the region. When we heard that Oliver Nunn, a leading consultant in this area and an AIMMS power user, was starting his own business, we could sense the start of a great partnership.
Oliver, can you tell us a little about your background?
I’ve worked as a consultant and advisor in the Australian energy sector for the last 11 years. During that time, I have provided advice on a wide range of optimization projects, including modeling of outcomes in the Australian national electricity market, the Philippines power market, and the Vietnam power system. I hold a first-class honours degree in pure mathematics, and a commerce degree with majors in economics and finance.
What brought you to this field and how did you develop an interest in OR/optimization?
I have seen first-hand how hard it can be for organizations to make decisions without models and quantitative analysis to guide them. Models help anchor reasoning to reality, and force people to justify their claims. As a result, I have always enjoyed working with organizations to build models that help them support their decisions.
I find optimization models particularly interesting. The idea that we are finding the best answer to a problem is particularly appealing – there is something inherently satisfying about knowing that your results drive the best possible solution.
How did you become familiar with AIMMS?
About 10 years ago, I built a large model in GAMS – it was an incredibly painful process, which left me thinking that there must be a better option available. I looked around for another algebraic modeling language and it became clear that AIMMS was the gold standard.
What do you like about AIMMS in particular?
There are three reasons that I like AIMMS.
- First, it is highly intuitive – you could argue that all algebraic modeling languages are. What sets AIMMS apart are the many built-in features that have been added to the language. Mechanisms for declaring sets and element parameters are just some examples of the powerful intuitive tools that AIMMS has. Other algebraic languages often do not have these features.
- Second, the AIMMS IDE makes life easier. Unlike other algebraic modeling languages, AIMMS tells me when I make a mistake immediately. I do not have to wade through code to find out where I made a mistake. The warning bar tells me where and what I have done wrong. Also, the IDE helps manage large complex models with ease. In GAMS or AMPL, large models rapidly become unwieldy.
- Third, the ability to build interfaces is a really useful aspect of AIMMS. It’s easy and quick to build a model that my client can use without having to see “under the hood.” The interfaces are intuitive, elegant, and easily deployable to the cloud.
You’ve made a great case for using AIMMS. Can you think of an AIMMS project you are particularly proud of?
Last year, I completed a model that looked at some of the system security implications of the transition to renewables. The AIMMS model that we built included some sophisticated use of integer variables, and the project team that I was working with found the results particularly helpful. Using the case functionality, we looked at several hundred scenarios and made some amazing graphics that helped show the shape of the solution space. Overall, it was a very satisfying project.
We’re very happy that you’ve become an AIMMS partner. How did the partnership between AIMMS and Endgame Economics come about?
I’ve been using AIMMS for about 8 years, and now that I run my own business, I wanted to make sure I had access to AIMMS. Given the amount of work we do using the software, AIMMS suggested that a partnership might be a good path forward for us. We were very keen on the idea. We were excited at the prospect of sharing AIMMS’ capabilities with our clients and helping them make better decisions using the software.
What opportunity do you think this partnership provides to the energy market?
We want to advise businesses using the best available quantitative tools. AIMMS is best-in-class and has the potential to be used all over the energy sector for all sorts of bespoke problems. I think there are big opportunities for participants in the energy market to start using these types of tools to do new, exciting, and innovative analyses. I hope we can start to see organizations solving all sorts of new and interesting problems using AIMMS, and we can support them in doing that.
How have you seen the market evolve over time, particularly in APAC and Australia?
Initially, businesses wanted to solve the same problem: projecting price. So, they used tools that were focused on this one type of analysis. But now, there are all sorts of optimizations that people need to think about – battery sizing, unit commitment modeling, and stochastic programming. People are starting to ask questions that traditional tools are not well-placed to answer. We need new tools, and they need to be custom-made for the problems we are facing. You can’t buy a piece of software off the shelf that will solve all the problems you may face. You need a tool that can help you build a bespoke solution to any problem. AIMMS fits that bill.
What shifts do you think the energy industry needs to make to speed up the adoption of analytics at all levels in their organizations?
The energy industry already does a great job using historical data. We’ve had access to high quality data in the energy sector for over 20 years, so we know how to analyze it. What is missing from our work in this sector is more prescriptive analytics – using models to help us make the best decision available. There is some of this type of modeling, but I think that it has not evolved much over time. We need more people with skills in building these types of tools throughout the industry.
The use of renewables is expanding rapidly. What steps should organizations take to manage the transition to a low-emissions power system?
I would encourage them to build their capability to build models in-house. At the moment, some of the biggest decisions, for instance about building new plants, are outsourced or completed using one-size-fits all models. What this type of approach fails to understand is that the value in modeling comes from the insights that you gather through the process – the results themselves are not overly important. But if you build a model, you understand what matters and what drives the outcomes. I encourage all my clients to build their own capabilities in-house, so that ultimately, they can be in charge of their quantitative analysis. In a sector that is changing as rapidly as the electricity industry, this is doubly true.