Concentrated Solar Power Plants Probabilistic Scheduling based on Weather Uncertainties

Kurzfassung

The high penetration of variable renewable generation resources brings an imbalance on power market prices and typical designs. 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. Therefore, CSP plants are expected to play the role of balancing electricity production and demand, by taking part in whole sale markets and being able to compete not only with other renewable resources but with all the relevant players. In the wholesale electricity market, the producers need to schedule the amount of energy to be dispatched for a certain interval of the day, which is then regulated by a central operator. 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 schedule of CSP plants needs to take it into account. Modification of energy deliveries once scheduled is very limited and usually associated with drawbacks in form of penalties or reduced prices. Therefore, accurate modeling and uncertainty treatment of the energy delivery schedule is essential to ensure the optimization of solar thermal energy dispatch, with high dependency on type and accuracy of weather forecasts. Research on the precision of direct normal irradiance (DNI) forecast of different forecast methods was already performed. Although, the impact of this accuracy on power scheduling and economic income of CSP plants are fields still to be studied. In this context, a dispatch optimization algorithm was developed in combination with a CSP plant model, used to derive the plant operation schedule for the upcoming 48 hours. It considers weather and electricity pricing forecasts with a special focus in the incorporation of uncertainty information. The innovative element of the strategy of the proposed Dispatch Optimizer (DO) is that it is based on a partitioned calculation between the optimization algorithm and the uncertainty processing. In the DO application, the optimization algorithm runs directly with all the possible weather scenarios, if the forecast is given as ensemble members, or deterministically with deviations, while the uncertainty is dealt as a post-processing. Therefore, the result of the optimization is a probabilistic set of possible power schedules, and the uncertainty post-processing is able to categorize single schedules suggestions according to the risk of meeting the promised energy delivery. This partitioned approach allows that the uncertainties included in weather forecasts are transferred to the power schedules, enabling then the uncertainty treatment to be done according to the energy market characteristics. The power schedule optimization considers not only the accuracy of the weather data but also the market characteristics on which the CSP plant is inserted. Therefore, several delivery strategies can be developed according to the decision maker’s point of view. The performance of a CSP plant following the developed DO strategy was simulated considering a whole year of operation. When compared to a basic optimization approach, the results of simulations with the proposed DO show that it can be considered as a valuable tool for a more accurate delivery scheduling, and consequently, for the improvement of financial income of CSP plants. According to the simulations, the possible increase on financial income is strongly related to the quality of the weather forecast, but also can be enhanced with the uncertainty post-processing. Depending on electricity market characteristics, different strategies can be defined and results closer to perfect forecasting can be achieved. In conclusion, the partitioned optimization approach with consideration of uncertainties on dispatch scheduling is shown to be economic and operationally beneficial. The consideration of weather uncertainties, by a probabilistic power scheduling, represents an important step for the insertion of CSP plants with storage in wholesale electricity markets. The flexibility brought by the storage in combination with the DO brings feasibility to the participation of CSP plants in the balancing power market, establishing their relevance to a highly renewable energy mix.