Inverters are a leading source of hardware failures and contribute to significant energy losses at photovoltaic (PV) sites. An understanding of failure modes within inverters requires evaluation of a dataset that captures insights from multiple characterization techniques (including field diagnostics, production data analysis, and current-voltage curves). One readily available dataset that can be leveraged to support such an evaluation are maintenance records, which are used to log all site-related technician activities, but vary in structuring of information. Using machine learning, this analysis evaluated a database of 55,000 maintenance records across 800+ sites to identify inverter-related records and consistently categorize them to gain insight into common failure modes within this critical asset. Communications, ground faults, heat management systems, and insulated gate bipolar transistors emerge as the most frequently discussed inverter subsystems. Further evaluation of these failure modes identified distinct variations in failure frequencies over time and across inverter types, with communication failure occurring more frequently in early years. These patterns can inform ongoing PV system reliability activities, including simulation analyses, spare parts inventory management, cost estimates for operations and maintenance, and development of standards for inverter testing. Advanced implementations of machine learning techniques coupled with standardization of asset labels and descriptions can extend these insights into actionable information that can support development of algorithms for condition-based maintenance, which could further reduce failures and associated energy losses at PV sites.

As a consequence of the warm and humid climate of tropical coastal regions, there is high energy demand year-round due to air conditioning to maintain indoor comfort levels. Past and current practices are focused on mitigating peak cooling demands by improving heat balances by using efficient building envelope technologies, passive systems, and demand side management strategies. In this study, we explore city-scale solar photovoltaic (PV) planning integrating information on climate, building parameters and energy models, and electrical system performance, with added benefits for the tropical coastal city of San Juan, Puerto Rico. Energy balance on normal roof, flush-mounted PV roof, and tilted PV roof are used to determine PV power generation, air, and roof surface temperatures. To scale up the application to the whole city, we use the urbanized version of the Weather Research and Forecast (WRF) model with the building effect parameterization (BEP) and the building energy model (BEM). The city topology is represented by the World Urban Database Access Portal Tool (WUDAPT), local climate zones (LCZs) for urban landscapes. The modeled peak roof temperature is maximum for normal roof conditions and minimum when inclined PV is installed on a roof. These trends are followed by the building air conditioning (AC) demand from urbanized WRF, maximum for normal roof and minimum for inclined roof-mounted PV. The net result is a reduced daytime Urban Heat Island (UHI) for horizontal and inclined PV roof and increased nighttime UHI for the horizontal PV roof as compared with the normal roof. The ratio between coincident AC demand and PV production for the entire metropolitan region is further analyzed reaching 20% for compact low rise and open low rise buildings due to adequate roof area but reaches almost 100% for compact high rise and compact midrise buildings class, respectively.

DOD's U.S. Pacific Command has partnered with the U.S. Department of Energy's (DOE) National Renewable Energy Laboratory (NREL) to assess opportunities for increasing energy security through renewable energy and energy efficiency in Hawaii installations. NREL selected Marine Corps Base Hawaii (MCBH), Kaneohe Bay to receive technical support for net zero energy assessment and planning funded through the Hawaii Clean Energy Initiative (HCEI). NREL performed a comprehensive assessment to appraise the potential of MCBH Kaneohe Bay to achieve net zero energy status through energy efficiency, renewable energy, and electric vehicle integration. This report summarizes the results of the assessment and provides energy recommendations.