Abstracts

Operators of photovoltaic (PV) and wind power systems in Europe are faced with considerable economic challenges. Specifically, governments scale back protective measures and subsidy levels, meaning that revenues must increasingly be realized in competitive markets under two constraints. Firstly, forecast errors trigger costly intraday efforts to restore the balance between day-ahead sales and actual production. Secondly, strong output correlations between geographically proximate wind or PV assets lead to below-average prices in times of strong winds or solar irradiation.
This paper examines the extent to which portfolio effects and intraday trade optimization can improve the market value of PV and wind assets. To this end, a two-stage model is formulated. In the first stage, forecasted supply and demand volumes are committed day-ahead, and economic value is being added thanks to portfolio effects between expected PV supply, wind supply, and power demand.
In the second stage, forecast errors in the portfolio's day-ahead power supply are traded jointly with flexible loads in the intraday market, accounting for two complexities. On the one hand, forecast errors remain inaccurate throughout the trading period; frequent forecast updates reduce, but do not eliminate uncertainty. On the other hand, limited liquidity and frequent changes in the trading activity of market participants cause large intraday price variations over time and across delivery slots. In order to maximize value under such uncertain and dynamic conditions, the model is configured such that it improves both the timing and the volumes of trades.
The timing of trades is determined by means of an options analysis. The intraday stage is split into several trade windows, and the optimal window for trading is chosen by iteratively comparing the value of trading immediately versus trading after waiting for reduced uncertainty in the next window. This method is found to increase efficiency, since changing volatility and risks are accounted for in the trading decisions. The value-added depends strongly on the underlying volatility.
Trade volumes are optimized through demand response, created when flexible loads are shifted between delivery slots based on a price ranking. This measure increases (decreases) the portfolio's supply-demand balance for delivery slots with short-term price jumps (drops). The intraday value of demand response is found to exceed the economic value of committing flexible loads day-ahead.