Balancing Forecast Errors in Continuous-Trade Intraday Markets

Abstract

Forecasting the production of photovoltaic (PV) and wind power systems inevitably implies inaccuracies. Therefore, sales made based on forecasts almost always require the vendor to make balancing efforts. In the absence of resources available within their own portfolios, operators can turn towards the intraday market in order to avoid an engagement in the imbalance market with the resulting surcharges and regulatory penalties. In this paper, we combine a novel trade value concept with options valuation and dynamic programming to optimize volume and timing decisions of an individual operator without market power when compensating PV or wind power forecast errors in the market. The model employs a multi-dimensional binomial lattice, with trade value maximized at every node to help formulating bids in view of correlated, uncertain production forecast and price patterns. Inspired by the German electricity market's characteristics, we test the sensitivity of the model's output – namely trade timing and trade volume – to changing uncertainty and transaction cost parameters in 50 different setups. It shows that the model effectively outbalances price against volumetric risks. Trades are executed early and with large batch sizes in the case of price volatility. In contrast, increasing forecast error uncertainty leads to trade delays. High transaction costs trigger batch size reductions and ultimately further trade delays. Running 10,000 simulations across ten scenarios, we find that the model translates its flexible trade execution into a competitive advantage vis-a-vis static bidding strategy alternatives.