CSP Dispatch Optimization considering Forecasts Uncertainties

Kurzfassung

The dispatchability of Concentrated Solar Power (CSP) plants with thermal storage is a key for the upcoming role of this technology on electricity markets. Due to the high penetration of renewable energy on the grid, CSP plants will be essential in balancing production and demand. The possibility of providing an accurate delivery schedule for the following days, based on electricity demand and weather forecasts, emphasize the importance of such plants in the pathway to a highly renewable energy mix. Since electricity pricing and 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, the uncertainty treatment in the energy delivery schedule is essential to ensure the optimization of solar thermal energy dispatch. Research on different strategies for dispatch optimization of CSP plants has been already performed. Approaches found in literature include a robustness parameter to consider the uncertainties, generalizing all the incertitude in one single criterion. Although, the sources of uncertainty on dispatch scheduling are plural, when considering different market setups and weather forecast methods. Hence, further improvement on this topic can increase CSP delivery accuracy. A Dispatch Optimizer (DO) was developed that is used to derive a CSP plant schedule for the upcoming 48 hours. It considers weather and electricity pricing forecasts with a special focus on the incorporation of uncertainty associated to the forecasts. A rule-based heuristic optimization was developed which is based on the problem-specific considerations. The strategy of the DO is based on a partitioned calculation between the optimization algorithm and the uncertainty processing. The heuristic optimization runs deterministically with all the possible input scenarios, while the uncertainty is dealt as a post-processing. Therefore, the result of the optimization is a range 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. These results show that a conservative approach is recommended to be applied in a market setup where the unmet delivery is highly penalized. In cases where the CSP plant does not operate under such market conditions, a more ambitious approach for the same input scenario can be taken. The results show the benefits of a partitioned approach for CSP plant scheduling. The developed strategy brings the possibility of dealing with several types of weather forecast, such as probabilistic and deterministic methods, as well as enabling the evaluation of the quality of weather forecast data and its efficiency for optimizing the dispatch. Also, the inclusion of other uncertainty parameters (e.g. economic benefit and operation strategy) and the consideration of penalties are incorporated in the optimization. Therefore, several delivery strategies can be developed according to the market setup and the decision maker’s point of view. The paper provides more details on the approaches and example calculations illustrating the benefit of the uncertainty processing.