The Value of More Accurate Power Forecasting in Global Electricity Markets

As an independent power producer of renewable energy, or as an energy market participant engaging in virtual power trading where wind and solar generation impact wholesale electricity prices, it is crucial to fully understand the financial ramifications of buying and selling in these forward markets. Market participants are either mandated or incentivized to anticipate wind and solar generation by the system operator or local balancing authority. The market rules that are established govern how and when energy is scheduled and dispatched. The rule specifics are wide ranging, with different approaches taken by each controlling entity. The rule diversity spans logistics like the time horizons for which energy can be offered or scheduled and the conditions under which credits are paid or charges are levied. Some markets are structured to allow intra-hour or intra-day scheduling, while others are purely day-ahead only. Further, some markets assess charges for large deviations from the scheduled or offered amount, while others do not, letting settlements happen at the real-time marginal price. The idea behind imbalance charges is to penalize poor forecasts, driving down the profits of the market participant in favor of promoting better grid reliability. As the charges grow larger, this has the effect of increasing the value of the forecast. However, if the cost of deviations becomes too large, then the producer may elect not to participate at all, and so there is incentive to keep charges low enough to attract more renewable energy resources onto the grid. These tradeoffs require a balance to satisfy both objectives.

Here, we examine the existing published rules for a representative set of electricity markets around the globe, with the goal of better understanding the value that a more accurate wind or solar power forecast would have under these conditions. A sampling of well-developed markets in Europe and the USA are surveyed, as well as newer markets in India and China. We pay specific attention to the framework in each that determines day-ahead energy scheduling and real-time imbalance practices. Using Vaisala's global weather data, power forecasting expertise and electricity market information, we undertake an economic analysis using forecasts of varying quality for representative sites in these markets. In so doing, we are able to translate between improvements in a traditional forecast error metric (like normalized MAE or RMSE) and estimated monetary savings. We are able then to categorize different markets by how much economic value an improved forecast would yield. We thus make some general conclusions about the perspectives of market participants and system operators under each type of market design.