Explore our library of blogs and updates for the latest in AI and short-term power trading.
Cannibalization is eroding capture prices, imbalance costs persist, and subsidy schemes increasingly withhold support during negative-price hours. Maintaining revenue now requires a more sophisticated trading setup.
In March 2025, the four Nordic TSOs introduced the automated Nordic mFRR Energy Activation Market (mFRR EAM). Day-ahead to imbalance spreads rose sharply from early 2025 as a result. We expect the current direction to reverse in 2027.
Following the events of 1 May 2026, the SDAC price floor is dropping to -600 EUR/MWh. This article covers how the floor change works and what it means for day-ahead and intraday trading going forward.
With platforms like PICASSO now operational, cross-border balancing has dampened some of the sharpest edges that characterized earlier years. Imbalance value still exists, but capturing it that has become more competitive.
Solar growth, evolving market rules, and accelerated automation are reshaping trading conditions at a pace the industry hasn’t seen before. We break down the key developments into three interconnected areas: the power grid, energy markets, and trading technologies.
The SDE++ has been highly effective in driving the Netherlands’ renewable energy buildout. However, it also created structural inefficiencies. What can power market participants do to protect their returns?
As of the 1st of October, European day-ahead power markets will transition from hourly to 15-minute market time units (MTUs). How does this impact bidding logic? Will a sawtooth pattern emerge?
When hundreds of short-term power traders pursue the same profitable strategy, that strategy eventually stops paying off. Yet market impact – the collective influence of trading decisions on market outcomes – remains a blind spot for many participants.
As volatility persists and imbalance price spreads widen, three market shifts are reshaping power forecasting requirements: traded volumes moving to intraday, increasing tail risk from rare weather events, and bias from poor data quality.