## 1 Introduction  
The Department of Energy (DOE) estimates that PV power will grow to 14% of the electricity supply by 2030 as the price of solar electricity reaches a point at which it is cost-competitive with cogen ($0.06/kwh by 2020). It is imperative that power grid reliability and stability be maintained under this high penetration of low carbon energy [1]. Organizations such as North American Electric Reliability Corporation (NERC) and California Energy Commission (CEC) [2] have formulated several requirements needed in a \"grid-friendly\" PV power plant. For instance, CEC has developed a set of several smart inverter functionalities such as dynamic volt/var operation and ramp rate control. Existing PV plants do not have these functionalities even though the inverters are capable, due to the lack of communications standards and dynamic control. For PV power plants to participate in energy markets and ancillary services markets, they need to be considered \"dispatchable\" power plants.  
High penetration of PV systems can be achieved if PV inverters [3] participate in the grid frequency regulation by active power control. Currently, frequency disturbances in the grid are handled by load curtailment. The disadvantage of this methodology is that it can cause voltage stress on the distributed generation. The alternative is to operate the PV system below its MPP (Maximum Power Point) to provide active power control. This can be done by modifying the MPP algorithm in such a way that it can track the next MPP point while working in the reduced power mode (RPM). A critical component of coordinated inverter control is forecasted solar power output or forecasted MPP at the array level. Having accurate intra-hour solar forecasts can enable implementation of a coordinated inverter control strategy capable of regulating a set-point, which may be a signal from a utility requiring either power curtailment or frequency regulation. The electric utility industry has yet to see an integrated solution to the dispatchability problem of PV plants, a system solution that effectively integrates intra-hour solar forecasting and smart control of inverters to achieve not only a grid-friendly plant, but also one that provides monetary and efficiency benefits to the PV plant operator.  
Microgrids lack the stabilizing effects present in a large urban macrogrid that itself is joined to an interconnect; these issues are critical when a microgrid is operated in islanded mode. The Energy Management Systems (EMS) that balance PV output, load, and battery storage require accurate intra-hour irradiance forecasts to solve the control problem by shedding non-critical load when power generated is predicted to drop significantly below the load. An increasingly important problem for utilities is optimal scheduling and dispatch of a microgrid [4; 5; 6; 7], both when connected to the macrogrid and when operated as an island. This task is divided into Day-Ahead Scheduling, which finds optimal schedules for the next operating day and focuses on energy markets, and Intra-day Dispatching and Scheduling in which schedules are continuously updated during the current day. Both cases follow these steps: (1) forecast day-ahead load, (2) forecast day-ahead renewable power (solar, wind), (3) Micro-Grid Management System (MGMS) optimizes the day-ahead plan, produces schedules for flows within the microgrid and to the PCC, (4) MGMS transmits schedules to utility control center.  
This article describes a four-year research effort to develop hardware and software with an aim to solve the intra-hour solar forecasting problem for electric utilities. It was a collaborative effort between many groups, including national labs and research institutes (NREL and the Texas Sustainable Energy Research Institute TSERI), two universities (The University of Texas at San Antonio and Universidad de La Laguna in the Canary Islands), public and private utilities (CPS Energy, ENDESA, and Duke Energy), and a private company, Siemens-Omnetric. It serves as a case study in managing research in a joint theatre of operations and integrating the efforts of researchers and engineers who came from very different university and industrial cultures. Details of our research and technology development have been presented in journal articles [8; 9], conference proceedings [10; 11; 12; 13], and technical reports [14].  
While this paper gives a detailed overview of that research, our primary goal is to describe how the UTSA Skylmager was validated at three geographically diverse locations, the pitfalls encountered, the lessons learned, and the outlook for future research efforts.  
The SkyImager evolved from a realization that existing all-sky imaging systems were too expensive to be deployed in large numbers, suffered from data-loss issues caused by the shadow band and camera arm, and used proprietary software. The Raspberry Pi single board computer ($35) and programable high-resolution Pi-Cam ($20) with a fisheye lens ($20) formed the heart of the new system.