Forecast value considering energy pricing in California

In this study, production forecast value is investigated using day-ahead market (DAM) and real-time market (RTM) locational marginal prices (LMP) at 63 sites in California. Using the North American Mesoscale (NAM) Model, day-ahead global horizontal irradiance (GHI) forecasts are established and converted to power assuming that a 1 MW solar photovoltaic plant is co-located at each observation site. Using this forecast, energy is hypothetically sold in the DAM. As the RTM occurs, deviations between forecast and observation are settled by hypothetically purchasing or selling energy at the RTM price. Total revenue is calculated by the sum of these two transactions. ison of NAM forecast revenue to perfect day-ahead forecast revenue shows that perfect forecast revenue is always greater. However, yearly NAM forecast revenue is as much as 98% of the perfect forecast revenue for some sites. After a bias-correction is applied to NAM forecasts, NAM forecast revenue decreases. This demonstrates that based on the observed DAM-RTM price spread, biased forecasts can have a higher forecast value than more accurate forecasts. However, when a deviation penalty is assessed, the most accurate forecasts always yield the highest total revenue.