Access to optimised data is becoming increasingly essential in today’s highly complex and volatile energy marketplaces. Most people do not think about the effect on the core processes when data-driven trading is in operation and how often we apply “new” data to largely obsolete processes.
The data, optimised or not, is an essential part of any trading strategy. Still, at the same time, it can be its Achilles Heel, mainly because we do not have the insight or understanding of utilising the data to optimise our processes or ways of working. This blog is an attempt to highlight the challenges and, to some degree, the solution to these challenges.
The European Power Market: An Overview
The European power market is diverse, complex and continuous, involving numerous regions and countries, regulations, environmental issues, and market structures. Energy trading is a result of demand and supply at a high level, but saying this would be too easy. We must examine the market dynamics to understand how multiple factors influence trading. The constantly evolving market mechanisms result from regulation and market adaptation towards increased renewables in the energy system spurred on by a push for decarbonisation. This transition leads traders to navigate a labyrinth of data sources and varying strategies. Typically, the available data for the traders supports the strategy and sets the stage for how traders interact with the markets. However, as the markets become more dynamic and automated and with greater regulation, they become more complex, windows for opportunity tighten, and risks increase. It is a question of how trading strategies must evolve at the same speed to match the trading velocity and de-risk operations, not at least when we see increased price volatility.
Data Management Optimised for Energy Trading
There are thousands of arguments about how optimised data management can benefit trading. Still, again, this is the challenge we all face: What is most beneficial to us, and how can we monetise data more efficiently?
I would claim that having access to optimised data in any trading situation makes your strategy and choices far better than without the data. More importantly, it will present an opportunity to improve and develop the processes around trading to become more fluent in hitting the market faster and better. Secondly, having access to optimised data will de-risk your trading operations. With constant overwatch of how the data informs trades, companies can easily monitor their market compliance and avoid errors, big or small, when participating in the markets. Clearly, finding the benefits and capitalising on these is the most important question; again, this should be data-driven. By automatically monitoring open positions against real-time data, you can quickly see market movements that either bring an opportunity or represent a threat against your positions or even your operational strategy. Having optimised data and applying “some intelligence” to the data should make it easy enough to implement data-driven decision-making processes to act on market changes.
Improvement of Forecasts and Risk Management
Improving forecast accuracy is essential for managing risks in trading strategies, especially short-term forecasts. Seasonable forecasts are good when it comes to understanding long-term hedging profiles. Still, short-term forecasts are often impacted by changing weather conditions and can change in hours, especially for wind and also, to a certain extent, hydro. Applying and leveraging machine learning and AI to analyse historical data in combination with real-time data feeds gives a better prediction of future trends. Still, more importantly, it can better guide and forecast demand curves, supply fluctuations, price volatility and movements, market disruptions, and de-risk trading operations.
Regulations and Compliance
The trading market is heavily regulated and monitored from a compliance perspective, with strict reporting requirements. These requirements are rigorous, and maintaining 100% compliance in every respect is a constant burden on operational teams. Errors are a continual threat, which is more likely to happen with humans than with machines.
By utilising advanced data management systems supported by structured and optimised data, trading businesses can automate data collection, validation, and reporting processes. This capability reduces the administrative burden on traders and compliance managers. It constitutes a competitive advantage in the form of improved learning for all traders and minimising future risks for penalties associated with being non-compliant. In other words, the advanced data management system can increase the competence of the trading staff and automate the risk management process by having clear rules and limits set in the system by risk & compliance to minimise breaches. In a perfect world, the system would prevent in real- time and erroneous bids or likewise leaving the front desk towards the market by notifying both the traders and risk management beforehand
Briefly on Market Integration
The European Power Market is moving towards becoming more integrated, supported by the European Internal Electricity Market (IEM), which aims to harmonise and create a unified market. The universal adoption of optimised data management systems will be crucial for full market integration, improving cross-border data exchange, and increasing market transparency. This digital layer will enable better coordination and management of the power system among all market participants, not at least the market surveyors and regulators.
Final Words on Operational Efficiency
Every time I hear that organisations are digitising, the rationale is that they want to achieve operational efficiency. Nothing is fundamentally wrong with this, but understanding what is needed to make operations more efficient is another issue and can often be mistaken for cost-cutting. Operational efficiency and digitalisation are all about process re-engineering and nothing else! So, what do I mean by this?
By utilising optimised data throughout a process, companies can see where the process presents bottlenecks and where administrative tasks can be automated and improved. It can also present latency issues in the process, which need to be dealt with to reduce data processing time, ensuring that ways of working become more efficient and that traders can perform better. Depending on the speed of the process, you might have to re-engineer it constantly, but with data support and a data-driven approach, it should not be a big problem. Ignoring this will potentially cause massive costs and lower efficiency as we are adding data into, as I initially said, obsolete and inflexible processes.
In closing, I have seen numerous examples of companies saying they are digitising trading when, in reality, they are voluntarily adding complexity to their internal systems. This effort doesn’t lead to efficiency gains; in fact, quite the opposite. It’s ironic to think that we are operating in complex markets, and then we add even more complexity to our internal systems. Indeed, we should always aim to make life easier for our traders and compliance managers.
Baard Eilertsen
EVP, Energy & Utilities for the EMEA Region at Hansen