4.1.3. SkyImager at La Graciosa, Canary Islands

Some valuable lessons learned from the Graciosa project have to do with the performance and durability of the sky-imagers in a harsh, dusty, and salty environment such as La Graciosa. The closeness of the island to the Sahara Desert makes the dust content in the atmosphere high. Quality of the images is severely affected by the deposition of dust on the enclosure, as seen in Figure 24, showing that scheduled cleanings of the enclosure are necessary to ensure the proper function of the devices. Our first estimation is that cleaning is necessary at least twice a year, but it is highly dependent on the climatologic and atmospheric conditions. Besides the deposition of dust on the enclosure, water infiltrations in the interior of the enclosure have been a frequent problem, even if the equipment was specially selected to have a high degree of protection to water and dust (IP67). Closeness to the sea, as well as the strong rains that occurred in December of 2017, appear to be the main source of this problem.

**Figure 24.** (**a**) SkyImager/image with dust on enclosure, (**b**) after cleaning it.

#### **5. Conclusions**

In March of 2018 the California Energy Commission mandated that beginning in 2020, all new home and apartment construction must include solar generation. When this level of distributed generation becomes part of the electric grid, ISO are faced with new challenges in terms of frequency and voltage control. Indeed, in Hawaii these issues resulted in a temporary hold on rebates for new residential solar installations.

In a macrogrid extending over hundreds of square kilometers, integrating a mix of generation conventional power plants, solar, and wind, and functioning as part of a larger interconnect such as ERCOT, there is an inherent inertia that works on the side of the utility. In microgrids, however, this inertia is lacking, and the control problem becomes much more difficult to solve when in islanded mode [4,7]. There are also different temporal scales involved. Optimal day-ahead scheduling and control of a microgrid is a distinct problem from hour-ahead control to ensure frequency and voltage do not vary outside prescribed limits. Using equipment such as the OP4500 RT-LAB/RCP/HIL real-time power grid digital simulator by Opal RT, we hope to use Hardware-in-the-Loop equipment to analyze the microgrid at JBSA. PMU measurements also need to be incorporated.

All-sky imaging technology will be a critical component in the overall solution strategy to predict solar irradiance 15 min ahead, and to take corrective measures during ramp events. It must, however, be fully integrated with NWP and satellite-based approaches for day-ahead load forecasting and optimal control of a microgrid. Optimal use of this technology will encompass a diverse group of specializations, including IoT and edge-computing, cyber-security, machine learning, and image processing.

For example, the characteristics and statistics of the all-sky imager must be included in the stochastic optimization programming for risk neutral and risk adverse operational control of a microgrid [6]. An holistic R&D approach is required. While a Raspberry Pi is the essence of plug-N-play and it is relatively straightforward to build a SkyImager, integration into the IoT and field deployment will remain an active area of research. Imagers will range the gamut in cost, accuracy, and interoperability. MGMS will integrate forecasts from imagers, NWP, and satellites, as well as hundreds of other meters and devices to solve the microgrid control problem. While physics-based methodology will continue to be important, machine learning and IoT technology will play an increasingly critical role. Development of standards such as OpenFMB for interoperability of thousands of devices will also be a necessary component.

#### **6. Patents**

A provisional US patent application "distributed solar energy prediction imaging" has resulted from the work reported in this manuscript.

**Author Contributions:** Formal analysis, A.M.; Funding acquisition, L.S.; Methodology, R.V.-A.; Writing—original draft, W.R.J. and David Canadillas; Writing—review & editing, R.G.-L. and H.K. All authors contributed to the final version of the manuscript.

**Funding:** This project and the preparation of this paper were funded in part by monies provided by CPS Energy through an Agreement with The University of Texas at San Antonio. The Universidad de La Laguna acknowledges support from ENDESA.

**Acknowledgments:** The authors acknowledge support from James Boston, Jorge Deleon, Michael Sparkman, Gaelen McFadden, and Michael Cervantes at CPS Energy, Greg Martin and Mike Simpson at NREL, Jim Waight and Shailendra Grover at Siemens-Omnetric, as well as collaboration with Bing Dong, Zhaoxuan Li, Brian Kelley, Brian Bendele, Jonathan Esquivel, and Marzieh Jafary at UTSA.

**Conflicts of Interest:** 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.

#### **References**


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