System imbalance forecasting and short-term bidding strategy to minimize imbalance costs of transacting in the spanish electricity market

Energy imbalances can represent a significant cost for agents transacting in markets that
penalize participants’ imbalances. In markets with increasing penetration of intermittent
renewable sources of energy (RES‐E), system imbalances can not only be costly, but also
increase, as is the case for the Spanish power market. Market participants, especially
those trading non‐dispatchable energy, are therefore interested in minimizing this cost
while simultaneously maximizing their profits.
A lot of work has been developed around the forecast accuracy and uncertainty of RESE
production to determine bidding strategies that minimize imbalance costs, especially
for wind power trading. Challenges inherent to agents specialized in power trading
and/or retailing activities, especially wind power trading of energy produced by third
parties or retailing to small consumers means that applying strategies that rely on
production forecasts may not be sufficient.
In this master thesis we considers those challenges by developing an optimized bidding
strategy that reduces the expected imbalance cost for a real case‐study of a Spanish
energy trader/retailer based on a forecast of the system´s imbalance volume and past
imbalance costs, while using new information available after the day‐ahead market gate
closure for participation in the intra‐day market to influence the imbalance volume of the
agent’s portfolio towards the direction that reduces their potential imbalance cost. This
strategy does not replace accurate forecasting but considers the practical aspects of
energy traders/retailers with numerous small clients who cannot operate production
units. The strategy can be applied from the perspective of both a trader and retailer.
We have developed an advanced model based on random forest technique to forecast the
system imbalance and used a genetic algorithm to apply the bidding strategy that
minimizes the imbalance costs based on system imbalance forecasts and past imbalance
costs. The proposed strategy application using new information available after the dayahead
gate closure outperforms its application in the pre‐day‐ahead market.