## 7 Discussion  
The model of the Coto Laurel solar power plant was developed, tested, and validated using an OP5700 simulator from OPAL-RT technologies. Simulation results throughout the year validated the accuracy of the proposed Coto Laurel model. With this model, a novel PRRC method was proposed. This proposed method and other PRRC methods were tested using daily and yearly data. Daily results indicate that there is a tradeoff between PRR regulation and DoD cycles. It is important to determine the storage capacity required to optimize PRR support and battery degradation. For this work, the marginal required storage capacity was used. However, less degradation could be appreciated when this capacity is increased. Furthermore, as the storage capacity is increased, the PRR violations are expected to decrease. This is because it is less probable to reach the minimum or maximum SOC values. When this event occurs, mainly during summer, the PRRC method is unable to support the PRR regulation.  
Figure 23: Cumulated minutes when the output PRR violated the requirements.  
Among the PRRC methods found in the literature, the RS showed to be the one with the least LCOS value. However, there are some days when the ESS may reach the maximum or minimum value, limiting the capacity of the RS method to support the PRRC. The ELES, SMA, and EMA showed similar LCOS values. However, the EMA showed the most violations of these three methods. Although the SOLPF did not show PRR violations, it is the PRRC method with the highest LCOS, followed by the FOLPF.  
The proposed PDS method showed the least LCOS values among all the PRRC methods. This was because the PDS used predicted data to charge or discharge the ESS to reduce DoD. This helped to reduce high-valued DoD cycles, which considerably increased the battery life while regulating the PRR without violations. Moreover, the PDS demonstrated to be robust against solar prediction inaccuracies of at least 20% of the real value with a minimum prediction window of 5 min. At shorter sampling periods, the algorithm would have more resolution correcting sudden PV power drops. However, if the window size is not increased, more violations would be expected, and the degradation would decrease because the ESS will not be used frequently because their output will not be smoothed. It is important to note that the selection of the sampling period was made based on the regulations, which dictated the PRR limit in minutes.  
It is important to consider that the predictive nature of the PDS may incur extra costs depending on the selected prediction method. There are plenty of solar prediction methods available in the literature that may be used for the PDS implementation [6; 35; 36; 39; 40]. However, it is important to consider the accuracy, the costs, and the effectiveness of these methods against radiation profiles found in places such as Puerto Rico. As future work, it is of interest to study the complete implementation of the PDS method in a real solar power plant using one of the solar prediction methods found in the literature.  
Not applicable.  
The data available in this study is openly available in IEEE Dataport, doi:10.21227/kbj4-h6e.  
Special thanks to [PERSON], from Windmar LLC, for providing the power data and system description used in this article. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.