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We present a simple algorithm for identifying periods of time with broadband GHI similar to that occurring during clear sky conditions from a time series of global horizontal irradiance (GHI) measurements. Other available methods to identify these periods do so by identifying periods with clear sky conditions using additional measurements, such as direct or diffuse irradiance. Our algorithm compares characteristics of the time series of measured GHI with the output of a clear sky model without requiring additional measurements. We validate our algorithm using data from several locations by comparing our results with those obtained from a clear sky detection algorithm, and with satellite and ground-based sky imagery.

Quasi static time-series simulations (QSTS) of distribution feeders are a critical element of distributed solar PV integration studies. QSTS are typically carried out through computer simulation tools such as OpenDSS. Since a typical feeder contains thousands of buses, for long investigation periods or at fine time scales such simulations are computationally costly. Simulation times are reduced in this paper through a reduction of the number of buses in the model. The feeder reduction algorithm considers p-phase distribution feeders with unbalanced loads and generation, unbalanced wire impedance, and mutual coupling, while preserving the spatial variation of load and generation. An extensive Monte Carlo sensitivity analysis was performed on a real feeder from a California utility. All bus voltage differences are found to be less than 1.13% with a root mean square error of 0.21%. Simulation time savings were up to 96% when only one bus is selected to remain in the model. Example applications of the proposed algorithm are interconnection studies of utility-scale photo-voltaic system to the distribution grid, siting analyses of other distributed energy resources (DERs), and dynamic behavior of devices in large systems such as smart inverters on distribution grids.

The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. We demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure.

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The U.S. Department of Energy launched the SunShot Initiative in 2011 with the goal of making solar electricity cost-competitive with conventionally generated electricity by 2020. At the time this meant reducing photovoltaic and concentrating solar power prices by approximately 75%relative to 2010 costs-across the residential, commercial, and utility-scale sectors. To examine the implications of this ambitious goal, the Department of Energy's Solar Energy Technologies Office (SETO) published the SunShot Vision Study in 2012. The study projected that achieving the SunShot price-reduction targets could result in solar meeting roughly 14% of U.S. electricity demand by 2030 and 27% by 2050-while reducing fossil fuel use, cutting emissions of greenhouse gases and other pollutants, creating solar-related jobs, and lowering consumer electricity bills. The SunShot Vision Study also acknowledged, however, that realizing the solar price and deployment targets would face a number of challenges. Both evolutionary and revolutionary technological changes would be required to hit the cost targets, as well as the capacity to manufacture these improved technologies at scale in the U.S. Additionally, operating the U.S. transmission and distribution grids with increasing quantities of solar energy would require advances in grid-integration technologies and techniques. Serious consideration would also have to be given to solar siting, regulation, and water use. Finally, substantial new financial resources and strategies would need to be directed toward solar deployment of this magnitude in a relatively short period of time. Still the study suggested that the resources required to overcome these challenges were well within the capabilities of the public and private sectors. SunShot-level price reductions, the study concluded, could accelerate the evolution toward a cleaner, more costeffective and more secure U.S. energy system. That was the assessment in 2012. Today, at the halfway mark to the SunShot Initiative's 2020 target date, it is a good time to take stock: How much progress has been made? What have we learned? What barriers and opportunities must still be addressed to ensure that solar technologies achieve cost parity in 2020 and realize their full potential in the decades beyond? To answer these questions, SETO launched the On the Path to SunShot series in early 2015 in collaboration with the National Renewable Energy Laboratory (NREL) and with contributions from Lawrence Berkeley National Laboratory (LBNL), Sandia National Laboratories (SNL), and Argonne National Laboratory (ANL). The series of technical reports focuses on the areas of grid integration, technology improvements, finance and policy evolution, and environment impacts and benefits. The resulting reports examine key topics that must be addressed to achieve the SunShot Initiative's price-reduction and deployment goals. The On the Path to SunShot series includes the following reports: • Emerging Issues and Challenges with Integrating High Levels of Solar into the ...

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