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1. SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays

   https://doi.org/10.1145/3378393.3402257  

   Feng, Menghong ; Bashir, Noman ; Shenoy, Prashant ; Irwin, David ; Kosanovic, Dragoljub ( June 2020 , Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS))

   null (Ed.)

   Solar arrays often experience faults that go undetected for long periods of time, resulting in generation and revenue losses. In this paper, we present SunDown, a sensorless approach for detecting per-panel faults in solar arrays. SunDown's model-driven approach leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior, can handle concurrent faults in multiple panels, and performs anomaly classification to determine probable causes. Using two years of solar data from a real home and a manually generated dataset of solar faults, we show that our approach is able to detect and classify faults, including from snow, leaves and debris, and electrical failures with 99.13% accuracy, and can detect concurrent faults with 97.2% accuracy. 

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2. Solar energy management as an Internet of Things (IoT) application

   https://doi.org/10.1109/IISA.2017.8316460  

   Spanias, Andreas S. ( August 2017 , 2017 IEEE 8th International Conference on Information, Intelligence, Systems & Applications (IISA))

   Photovoltaic (PV) array analytics and control have become necessary for remote solar farms and for intelligent fault detection and power optimization. The management of a PV array requires auxiliary electronics that are attached to each solar panel. A collaborative industry-university-government project was established to create a smart monitoring device (SMD) and establish associated algorithms and software for fault detection and solar array management. First generation smart monitoring devices (SMDs) were built in Japan. At the same time, Arizona State University initiated research in algorithms and software to monitor and control individual solar panels. Second generation SMDs were developed later and included sensors for monitoring voltage, current, temperature, and irradiance at each individual panel. The latest SMDs include a radio and relays which allow modifying solar array connection topologies. With each panel equipped with such a sophisticated SMD, solar panels in a PV array behave essentially as nodes in an Internet of Things (IoT) type of topology. This solar energy IoT system is currently programmable and can: a) provide mobile analytics, b) enable solar farm control, c) detect and remedy faults, d) optimize power under different shading conditions, and e) reduce inverter transients. A series of federal and industry grants sponsored research on statistical signal analysis, communications, and optimization of this system. A Cyber-Physical project, whose aim is to improve solar array efficiency and robustness using new machine learning and imaging methods, was launched recently 

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3. DeepSnow: Modeling the Impact of Snow on Solar Generation

   https://doi.org/10.1145/3408308.3427620  

   Bashir, Noman ; Irwin, David ; Shenoy, Prashant ( November 2020 , Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys))

   null (Ed.)

   The falling cost of solar energy deployments has resulted in ever-increasing growth in solar capacity worldwide. The primary challenge posed by increasing grid-tied solar capacity is handling its variability due to continuously changing conditions. Thus, prior work has developed highly sophisticated models to estimate and forecast solar power output based on many characteristics, including location, elevation, time, weather, shading, module type, wiring, etc. These models are highly accurate for estimating solar power, especially over long periods, for sites at low latitudes, i.e., closer to the equator. However, models for sites at higher latitudes are less accurate due to the effect of snow on solar output, since even a small amount of snow can cover panels and reduce power to zero. Improving the accuracy of these models for annual solar output by even 2--3% is significant, as power translates directly into revenue, which compounds over the system's lifetime. Thus, if a site produces 2--3% less power on average per year due to snow than a model predicts, it can mean the difference between a positive or negative return-on-investment. To address the problem, we develop DeepSnow, a data-driven approach that models the effect of snow on solar power generation. DeepSnow integrates with existing solar modeling frameworks, and uses publicly available snow data to learn its effect on solar generation. We leverage deep learning to quantify the effect of different snow variables on solar power using 4 million hourly readings from 40 solar sites. We evaluate our approach on 10 solar sites, and show that it yields a higher accuracy than the current approach for modeling snow effects used by the U.S. Department of Energy's System Advisor Model (SAM), a popular solar modeling framework. 

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4. Ratepayer Perspectives on Mid- to Large-Scale Solar Development on Long Island, NY: Lessons for Reducing Siting Conflict through Supported Development Types

   https://doi.org/10.3390/en13215628  

   Schelly, Chelsea ; Prehoda, Emily ; Price, Jessica ; Delach, Aimee ; Thapaliya, Rupak ( November 2020 , Energies)

   null (Ed.)

   The state of New York has ambitious mandates for reducing greenhouse gas emissions and increasing renewable energy generation. Solar energy will play an important role in reducing greenhouse gas emissions from the electric energy sector. Concerns over solar installations’ impacts to host communities and the environment have led to growing conflicts over solar energy siting on Long Island, in other parts of New York, and throughout the US. Understanding community members’ perspectives is critical for reducing conflict. Solar energy can be deployed more quickly and at lower cost if projects are structured to address the concerns and meet the needs of the community. This paper presents the results of a survey of residential utility ratepayers that examined their perceptions, preferences, and priorities concerning mid- to large-scale solar development on Long Island (250 kW and larger). The survey asked respondents to consider specific installation types, financial models, and other aspects of solar development. Results indicate that respondents were overwhelmingly supportive of mid- to large-scale solar development in their communities. The most highly supported development types were solar systems on rooftops and solar systems that are co-located with other land uses (mixed use) at a particular site, such as parking canopies, landfills, or integration with agriculture. The most highly supported financial models included privately funded projects by local developers and community solar projects. The largest concern about solar development expressed by respondents did not involve tree removal or visibility (as initially hypothesized to be the most significant considerations) but rather the fairness of the distribution of economic benefits associated with solar development. This paper provides concrete insight into particular models of solar development that may invoke less conflict and more community support. 

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5. Public preferences for five electricity grid decarbonization policies in California

   https://doi.org/10.1111/ropr.12442  

   Boudet, Hilary ; Zanocco, Chad ; Stelmach, Greg ; Muttaqee, Mahmood ; Flora, June ( August 2021 , Review of Policy Research)

   The climate crisis and associated push for distributed, renewable electricity generation necessitate policy changes to decarbonize and modernize the electricity grid. Some of these changes—e.g., smart meter rollouts and tax credits for solar panel adoption—have received attention in the media and from social scientists to understand public perceptions and responses. Others—e.g., allowing peer‐to‐peer electricity sales, promoting residential electrification, requiring solar panels on new development, funding microgrids, and paying customers to allow for utility control of electricity use—have received less attention. Here, we explore public perceptions of these understudied policies among California residents (n = 804), a state recognized for innovative energy policy. A majority of respondents supported only one of the policies—requiring solar panels on new development. Others elicited more indecision; few were strongly opposed. In general, male respondents and those with college degrees were more supportive of such policies, as were those more concerned about climate change and with a more open orientation to smart home technologies.

    

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