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Distributed solar generation is rising rapidly due to a continuing decline in the cost of solar modules. Nearly all of this solar generation feeds into the grid, since battery based energy storage is expensive to install and maintain. Unfortunately, accommodating unlimited intermittent solar power is challenging, since the grid must continuously balance supply and demand. Thus, governments and public utility commissions are increasingly limiting grid connections of new solar installations. These limitations are likely to become more restrictive over time in many areas as solar disrupts the utility business model. Thus, to employ solar without restrictions, users may increasingly need to defect from the grid. Unfortunately, batteries alone are unlikely to become cost-efficient at enabling grid defection for the foreseeable future. To address the problem, we explore using a mixture of solar, batteries, and a whole-home natural gas generator to shift users partially or entirely off the electric grid. We assess the feasibility and compare the cost and carbon emissions of such an approach with using grid power, as well as existing “net metered” solar installations. Our results show that the approach is trending towards cost-competitive based on current prices, reduces carbon emissions relative to using grid power, and enables users to install solar without restriction. 

Rooftop solar deployments are an excellent source for generating clean energy. As a result, their popularity among homeowners has grown significantly over the years. Unfortunately, estimating the solar potential of a roof requires homeowners to consult solar consultants, who manually evaluate the site. Recently there have been efforts to automatically estimate the solar potential for any roof within a city. However, current methods work only for places where LIDAR data is available, thereby limiting their reach to just a few places in the world. In this paper, we propose DeepRoof, a data-driven approach that uses widely available satellite images to assess the solar potential of a roof. Using satellite images, DeepRoof determines the roof's geometry and leverages publicly available real-estate and solar irradiance data to provide a pixel-level estimate of the solar potential for each planar roof segment. Such estimates can be used to identify ideal locations on the roof for installing solar panels. Further, we evaluate our approach on an annotated roof dataset, validate the results with solar experts and compare it to a LIDAR-based approach. Our results show that DeepRoof can accurately extract the roof geometry such as the planar roof segments and their orientation, achieving a true positive rate of 91.1% in identifying roofs and a low mean orientation error of 9.3 degree. We also show that DeepRoof's median estimate of the available solar installation area is within 11% of a LIDAR-based approach. 

The declining cost and rising penetration of solar energy is poised to fundamentally impact grid operations, as utilities must continuously offset, potentially rapid and increasingly large, power fluctuations from highly distributed and "uncontrollable" solar sites to maintain the instantaneous balance between electricity's supply and demand. Prior work proposes to address the problem by designing various policies that actively control solar power to optimize grid operations. However, these policies implicitly assume the presence of "smart" solar modules capable of regulating solar output based on various algorithms. Unfortunately, implementing such algorithms is currently not possible, as smart inverters embed only a small number of operating modes and are not programmable. To address the problem, this paper presents the design and implementation of a software-defined solar module, called Helios. Helios exposes a high-level programmatic interface to a DC-DC power optimizer, which enables software to remotely control a solar module's power output in real time between zero and its current maximum, as dictated by the Sun's position and weather. Unlike current smart inverters, Helios focuses on enabling direct programmatic control of real solar power capable of implementing a wide range of control policies, rather than a few highly-specific operating modes. We evaluate Helios' performance, including its latency, energy usage, and flexibility. For the latter, we implement and evaluate a wide range of solar control algorithms both in the lab, using a solar emulator and programmable load, and outdoors. 

Since many residential locations are unsuitable for solar deployments due to space constraints, community-owned solar arrays with energy storage that are collectively shared by a group of homes have emerged as a solution. However, such a group-owned system does not allow individual control over how the electricity generation from the solar array and energy stored in the battery is used for optimizing a home's electricity bill. To overcome this limitation, we propose vSolar, a technique that virtualizes community solar and battery arrays such that each virtual system can be independently controlled, regardless of others. Further, we present mechanisms and algorithms that allow homes with surplus energy to lend to homes with deficit energy. 

Smart energy meters record electricity consumption and generation at fine-grained intervals, and are among the most widely deployed sensors in the world. Energy data embeds detailed information about a building's energy-efficiency, as well as the behavior of its occupants, which academia and industry are actively working to extract. In many cases, either inadvertently or by design, these third-parties only have access to anonymous energy data without an associated location. The location of energy data is highly useful and highly sensitive information: it can provide important contextual information to improve big data analytics or interpret their results, but it can also enable third-parties to link private behavior derived from energy data with a particular location. In this paper, we present Weatherman, which leverages a suite of analytics techniques to localize the source of anonymous energy data. Our key insight is that energy consumption data, as well as wind and solar generation data, largely correlates with weather, e.g., temperature, wind speed, and cloud cover, and that every location on Earth has a distinct weather signature that uniquely identifies it. Weatherman represents a serious privacy threat, but also a potentially useful tool for researchers working with anonymous smart meter data. We evaluate Weatherman's potential in both areas by localizing data from over one hundred smart meters using a weather database that includes data from over 35,000 locations. Our results show that Weatherman localizes coarse (one-hour resolution) energy consumption, wind, and solar data to within 16.68km, 9.84km, and 5.12km, respectively, on average, which is more accurate using much coarser resolution data than prior work on localizing only anonymous solar data using solar signatures.