Treating Uncertainties as an Asset:�the Use of Probabilistic Forecasts for Dispatch Optimization

Kurzfassung

The high penetration of intermittent renewable energy in electricity grids brings the role of balancing production and demand for energy systems with flexible dispatch. Due to their thermal storage capability, concentrated solar power (CSP) plants provide certain flexibility between collecting thermal energy from the sun and transforming it into electric energy by means of a power cycle. The dispatchability of CSP plants is based on the electricity demand and weather forecasts, to accurately schedule the electricity production over the following days. Since weather forecasts include uncertainties, the dispatch planning needs to take them into account. Modification of energy deliveries once scheduled is very limited and usually associated with financial drawbacks. Therefore, the uncertainty treatment in the energy delivery schedule is essential to ensure the optimization of energy dispatch. As chance is not involved in any future state of the traditional deterministic weather forecasting method, the scientific community has been working on improvements to produce more accurate forecasts. The ensemble prediction system (EPS) captures the sources of uncertainty by producing ensemble forecasts. With that, uncertainties can be taken into account in order to plan robust and improved energy dispatch schedules. A dispatch optimization algorithm was developed to derive a CSP plant operation schedule for the day-ahead market. It considers weather and electricity pricing forecasts with a special focus in the incorporation of uncertainty information, by the use of probabilistic weather forecasts.