How AI can advance energy trading

Computer programs have been making decisions for almost ten years at RWE Supply & Trading. The subsidiary of energy company RWE specialises in energy trading, for which it has been using algorithms to support its operations in certain areas since 2014. These algorithms can evaluate huge amounts of data in real time and make automatic trades on energy markets – faster and more reliably than any human being.

Algorithms are not the same as AI

However, algorithmic power trader Hendrik Vollrath claims that such systems are not actually artificial intelligence. He was involved in the development of the very first trading algorithms at RWE Supply & Trading and says, “Our algorithms are first and foremost classical automation software – not learning AI systems. Our algorithms are programmed to follow a decision-making logic based on predetermined strategy parameters. By contrast, AI learns its own, optimal decision-making logic through suitable training, with the details of such logic largely remaining a black box for the human user.” Following the decision-making process of such systems from the outside is thus virtually impossible.

However, this is precisely what is paramount to traders at RWE Supply & Trading: “As market participants, we undertake to guarantee that our algorithms display trading behaviour that is always in line with the market,” emphasises Christoph Kath, another algorithmic power trader at RWE Supply & Trading. “This is why our algorithms are designed so that we can trace every step. If machine learning – the key technology for AI – is used, this can only be done to a limited extent.” This is because the system generates its own knowledge by identifying patterns and regularities in datasets.

AI already successful in test trading

A recent study by the Fraunhofer Institute for Energy Economics and Energy System Technology (Link in German) suggests that these types of application can absolutely make successful trades. In trials, assisted trading agents have already achieved promising results through deep learning – a domain of machine learning. However, the scientists caution that further experiments are necessary before deploying such AI software in live trading, emphasising that one issue needing clarification is the degree to which they work reliably in real life settings.

Nevertheless, RWE Supply & Trading’s algo traders believe artificial intelligence harbours substantial potential for their daily work. “Energy markets are extremely dynamic, and we must constantly adapt our algorithms to changed circumstances. Given this agile way of working, AI could make a valuable contribution,” says Hendrik Vollrath. He states that it would be conceivable for AI to monitor the algorithms’ development, thereby providing detailed insight into the impact of changes on trading results.

Advantages for digitised companies

RWE Supply & Trading’s experts have absolutely no doubt that AI will play a role in energy trading. “Those who succeed in integrating the new technology in existing processes well will become more agile and productive – a competitive edge that should not be underestimated in our dynamic market environment,” says Hendrik Vollrath.

And he underscores that algo trading has the best prerequisites for this purpose: “Algo trading can definitely be a shining example of digitisation in the working world. Be it our trades, our internal processes or the history of our software development, we have huge amounts of data in each of these areas. We believe this is an outstanding springboard to the future deployment of AI.”