Forecasting Electricity Prices and Market Length for Trading Stochastic Generation in Markets with a Single-price Balancing Mechanism

This paper derives revenue-maximising and risk-constrained strategies for stochastic generators participating in electricity markets with a single-price balancing mechanism. The solution to this problem requires forecasts of multiple processes: the participant’s energy production, day-ahead and balancing electricity prices, and the system length. By formulating the problem from a probabilistic perspective, it is demonstrated that a combination of well known and understood forecasting techniques can support market participants in both increasing revenue and reducing risk. Probabilistic forecasts of system length are produced using logistic regression on data widely available to market participants, and electricity prices are forecast using ARMAX models with automated fitting. Wind power forecasts are provided by a wind farm operator for a case study based on wind participating in the UK electricity market. It is shown that the proposed approach can be employed to increase revenue, by over 10% in the most extreme case, and to reduce risk.