Does the clean energy transition push ETRM systems towards more data management?

When looking back at the history of ETRM systems since the inception of power markets, we can see two different paths that vendor companies have pursued. One path involved building new trading applications on the top of other software solutions used by energy companies while the other entailed constructing new trading software from scratch. The first group of vendors has often already developed tools like consumption forecasting, power plant unit commitment, and dispatch, typically based on a general time series data structure. These vendors often continued to utilize the same time series structure for trading data, such as price or volume curves.

However, the second group of vendors, who started from scratch, often involved traders in architecting the system. As a result, their software was crafted by traders and for traders making it preferable for trading houses. In contrast, the first group of solutions was primarily adopted by small and medium utilities requiring a set of functionalities from production optimization and forecasting to basic portfolio management. In the past ComTech and its precursor, CommodityPoint, talked about asset-heavy and asset-lite ETRM to describe these two approaches. Today, all these ETRM solutions are considered as legacy systems. Why should we then discuss them?

Well, we feel that understanding their evolution and the choices made in their development helps to provide insights into the current landscape of energy trading technology. This is why ComTech places emphasis on systems’ pedigree and history – it is important in understanding each solution on the market ( – see Reames & Vasey, An Analyst View of a Dynamic Software Market, 2023). Moreover, the features which did not deserve much appreciation by traders earlier may now have become a game changer.

Due to the market changes related to energy transition, the time series management attached to an ETRM solution becomes again an important differentiator. Large legacy ETRM solutions which are backbones of the software landscape of tier 1 and 2 energy companies are primarily focused on price-related time dependent variables, which were always the main concern for traders. However, with the emergence of renewable production assets, such as batteries, and new deal types like Power Purchase Agreements (PPAs), there is a growing demand for additional time series data to support trading activities, such as weather data, aggregated meter data, etc. These time series can be added to the ETRM solutions but usually stored in different structures within the ETRM separate from the price data. However, these time series can still become inputs to formulas which may include price, volume data, and even more. There are a lot of other calculations where time series data need to be considered holistically, independent of the nature of these data. Of course, legacy ETRM solutions may allow “work arounds” to deal with the requirement, but when the distributed assets and related deal types or formulas become common as opposed to being considered exotic, it is increasingly difficult to use those “work arounds”. Even rather modern ETRM solutions often lack a comprehensive time series data model, despite its usefulness.

Exceptions include solutions originally designed for forecasting, optimization, or general data management with trading and risk capabilities added on the top, as described above. Such solutions can benefit from built-in time series management capabilities, but most of them are built within a legacy architecture and are at their end-of-life stage. Very few examples of still used solutions here might include IRM, now part of Kisters, some Hitachi Energy software products or FIS Energy Portfolio Management.

But how might other ETRM vendors address these challenges?

Integration of a time series data platform into ETRM systems can provide a centralized hub for all time-dependent data required by the ETRM. This integration opens numerous business opportunities from building forward curves to conducting formula-based calculations regardless of the inputs’ nature. A further significant use case for the integration of time series management arises from consolidating multiple software components into a single, flexible platform. The time series data hub becomes a critical component building a common data structure for all the consolidated components. It does not just simplify modeling, but it becomes a mandatory part of the solution.

Customers who have traditionally managed time series data alongside their ETRM systems are reluctant to switch to solutions lacking time series management capabilities when trying to replace their legacy ETRM(s). Multiple replacement projects are struggling to manage this transition even in the case of powerful new ETRMs.

With the rise of Virtual Power Plants and aggregators operating them, new trading-related business cases are emerging. These market participants, tasked with managing large volumes of meter data alongside trading activities, represent a growing market segment ripe for development.

In summary, integrating time series platforms with ETRM systems enhances their capabilities, addresses evolving data requirements, and opens new business opportunities in the energy trading landscape.

 

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