A sandbox simulaton of Finland's electricity market
(I have been quiet on this blog for quite a while. I might offer an explanation on that at some point. Although, some of the reasons can be found in this blog post.)
During the latter part of 2017 I worked on my bachelor’s thesis on Engineering Physics at the Aalto University1. You can find the results online (with a short description and a link back to this article) at energysim.kooma.net and GitHub. If you want to play with the result, click on the first one, and if you are interested in the code and not on a pretty long monologue, click on the second one.
Also, in case you want to get really sciency, just jump to reading the actual B.Sc. thesis.
There’s this thing called energy policy. You might not know it even exists, yet it affects you every day. Each country has one, even if it is only implicit in the set of current policies enacted by the government. Energy policy essentially defines the set of goals of how energy is produced and used by a country, and the set of rules that aim to get those goals accomplished. In the olden days, energy policies were mostly about economics and safety of the energy supply. Nowadays, a third axis of environmental effects must be taken into account (especially greenhouse gases, but also particulate emissions et al).
Even countries neighbouring each other can have vastly different energy policies that reflect their own, particular local environment. Norway, for example, has a huge natural advantage of having lots of water and mountains. When these two are mixed, these result in lots and lots of hydroelectric power. So while Norway is a major oil producer, it produces almost all of its electricity from water. Contrast this with France, which has taken a completely different approach having over 70% of its electricity producer through nuclear power. So overall, without going too much into the why’s of energy policy, let’s just say that policies differ from country to country, and there’s usually good reasons beneath the differences.
Also, while I’ll primarily will talk about electricity, please note that “energy use” includes also the production and use of heat (pretty important in colder climates), industrial uses and transportation.
So, when concentrating on my own country, Finland, and its energy policy, one finds many interesting and conflicting drivers. At the moment, simply put, Finland is dependent on electricity imports. The peak electricity production in Finland is less than the peak demand2. Whether this is a problem right now is up to debate, and it may even be possible that in the future the problem will be solved through an unified all-EU electricity market. However, things move pretty slowly in the electricity market, thus any external beneficial effects are likely to be even more slow than any local energy policy effects (it takes easily decades to build GWs of production capacity3).
Energy policy, pretty much by its definiton will be closely tied with politics, which in turn ties into popular opinion. I do not know about other countries, but at least here in Finland there are several trends affecting the attitude towards energy policy in the general population: polarization, loss of interest, lack of trust and unrealistic production method preferences:
- There’s a widening attitude gap between rural and urban population. While the majority of Finland’s population is urban (>80%), due to historical reasons the rural population has a larger say in domestic politics than would be immediately obvious. For this reason, these differences cannot be disregarded.
- Some segments (read: the same segment that fancies any popular politic, e.g. white, male etc. etc.) are becoming jaded with the complexities of energy policies (yearning back to the times when you could just burn oil without any care in the world?), and of course, if the trend gains more traction will make any kind of rational energy policy more and more difficult as it is hijacked by popular politics.
- There’s a marked lack of trust by the general public on politicians (on energy policies), of energy companies … also there are examples in Finland of politicians strongly dismissing energy and policy experts. Not good, of course.
Unless you’ve lived under a rock, you should have seen where popular politics combined with ignorance of facts can lead. So, ignorance, not a good thing. What can be done about it?
Show, don’t tell
I think that people are more receptive towards results they have experienced through their own actions. What if people could play out their own energy policy preferences out, and see how that would affect CO₂ emissions and the safety of electricity supply? Out of this thought resulted the Energy Simulator (or more accutarely, electricity sandbox simulation of Finland but the shorter, albeit less accurate version has stuck). Show below are two different situations, first the default state, and another with some … issues:
You can go ahead and play with the energy simulator at energysim.kooma.net. I’ll talk a bit more about the technical details below and if you are so inclined, you can check out the source code at github.com/santtu/energysim. You can find also a lot more background information on Finland’s energy situation, on how the energy simulator’s simulation and its parameters are modeled in my Bachelor’s thesis.
I’ll quickly touch on some non-technical issues, but after that I’ll go more into the technical details of the implementation. You can skip rest of the post if you’re not into programming (well, after some other stuff first).
While the energy simulator is nice in itself, it is not a game as it currently stands. It is an open-ended sandbox simulation, and it has lots of caveats and shortcomings due to all of the necessary abstraction and simplification that just getting it finished in limited time required.
Likewise the hypothesis that using a “game” (which this, strictly speaking is not) would be more effective in changing people’s attitudes on energy policy towards more “holistic” approach (from where? from assumed polarized ends?) is entirely unverified. I thought about adding a questionnaire before and after, but finally decided to omit it simply because of schedule (results would not have made into the BSc report).
And finally, I am not an UX designer and either not very good at CSS or web layouts. There are tons of elements that I dislike yet I could not justify myself for spending hours and hours on polishing it from “engineering quality” to “professional quality”, especially when I needed to finish first other things more relevant from the scientific point of view. In the end, it got finished, and I have other things to do. Time to move on and accept it as whatever it currently is.
Let’s get technical
- Monte Carlo simulation. The simulation is a stochastic simulation that, for each simulation round, draws random samples of capacities for consumption and production values4.
- Edmonds-Karp maximum flow algorithm is used iteratively to distribute electricity (from surplus production areas) in a way that minimises global CO₂ emissions.
- Everything runs in the browser. This is a pure single-page application, with everything running in the browser including the sandbox simulation. The simulation runs in a separate web worker thread so it doesn’t block the user interface.
- All state in the URL. The URL contains always contains the current model. You can copy and paste the URL for others. Uuuuunfortunately the URL is also several kilobytes in size and that might potentially be a teeny little problem with some browsers…
The use of Monte Carlo simulation came quite naturally as most of the source data is experimental (e.g. real world) and not expressable as a mathematical expression. This meant that performing any kind of mathematical analysis to infer the overall properties of the system was pretty much out of the question. The use of random sampling in a Monte Carlo simulation results in meaningful and accurate statistics in the long run. Thus after twiddling with the parameters you can let the simulation just run and the most prominent values (mean and standard deviation) are guaranteed to be close to what would be the results of an analytical approach (if ever done) to within some error margin5.
From the very beginning I realized the simulation would need to run in a browser. There is no way I could furnish the resources to do this server-side! While the JVM version runs at about 500x the speed of the JS version, there are way way way more browsers out there. I thought about using WebCL for the simulation core to speed it up as all of the sampling could be trivially parallelized, and even the Edmonds-Karp algorithm probably could be ported to WebCL. However, schedules again, and the few iterations per second I get from my old MBP on Chrome is “good enough”.
I did the user interface using React bindings to Scala.JS. While it took some time to learn their proper use (the distinction between state, props and backend instances and what to use were not always immediately obvious), I liked the result overall. What I came out missing was an easier way to integrate with other (non-core) react components. I ended up using a patched version of scalajs-react-bridge — not an optimal solution, and the syntatic difference between scalajs-react and scalajs-react-bridge usage is a visual irritation.
While all of the model is encoded in the URL, this may result in problems as the URL is a teeny bit long (it is Base64-encoded JSON file). Passing the model via URL does have the benefit that it is possible to pass URLs around. I am not sure how URL shortening services will react to several kilobytes of URL, though…
Okay, the technical bit came out as a bit of a ramble. In case you are interested in more details, or would like to extend or re-use the energy simulator please don’t hesitate to contact me with questions! Also, in case you are reading this in 2020 or so you probably should check out the repository first as it might be more up-to-date.
Not my first degree. ↩
This can be attested by the Olkiluoto 3 nuclear power plant project which has taken over a decade longer than planned. ↩
Transmission line capacity is also sampled, but for simplicity they were modeled at constant capacity (e.g. no failures, no capacity variances). ↩
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