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China has emerged as the global manufacturing center for solar photovoltaic (PV) products. Chinese firms have entered all stages of the supply chain, producing most of the installed solar modules around the world. Meanwhile, production costs are at record lows. The decisions that Chinese solar producers make today will influence the path for the solar industry and its role towards de-carbonization of global energy systems in the years to come. However, to date, there have been no assessments of the future costs and efficiency of solar PV systems produced by the Chinese PV industry. We perform an expert elicitation to assess the technological and non-technological factors that led to the success of China's silicon PV industry as well as likely future costs and performance. Experts evaluated key metrics such as efficiency, costs, and commercial viability of 17 silicon and non-silicon solar PV technologies by 2030. Silicon-based technologies will continue to be the mainstream product for large-scale electricity generation application in the near future, with module efficiency reaching as high as 23% and production cost as low as $0.24/W. The levelized cost of electricity for solar will be around $34/MWh, allowing solar PV to be competitive with traditional energy resources like coal. The industry's future developments may be affected by overinvestment, overcapacity, and singular short-term focus.

Objective: Ensure that GEB technology, building performance, and customer cost-benefit data are easily accessible, and improve and standardize analytical methods.GEB field performance assessments and metrics are needed to enable grid operators to trust the ability of demand flexibility to reliably deliver grid services. This includes developing and evaluating the use of standard baseline M&V methods to measure demand flexibility, as well as and collecting field data on demand flexibility building performance. Also, building owners and operators are unwilling to invest in technology without a clear value proposition based on proven technology benefits. Demand flexibility benchmark data sets, load shapes, and metrics are needed across all building sectors to provide relevant, comprehensive data for GEB technology performance evaluation. To draw meaningful conclusions from the data that can be relied upon by grid operators, utilities, and customers, there is a need for statistically significant data sets at scale and across different dimensions of building type and time (e.g., hourly, daily, annually). Key implementation challenges include managing privacy and cybersecurity with widespread data accessibility.Users may have privacy or security concerns related to the transmission and storage of whole-building and specific end-use equipment and system data. Utilities, aggregators, technology providers, and DER service providers may also worry about liability related to sharing customer data.Additionally, providing granular data would require robust data storage systems. Technology providers must carefully balance these concerns with the need to provide easy access to data for customers, grid operators, aggregators, and performance evaluators. A challenge specifically related to analytical methods is establishing appropriate baselines, particularly with multiple programs and rate designs, and when demand flexibility is used routinely. Key ActionsDevelop standard metrics and methods for data collection, data analysis, and measurement and verification (M&V) of demand flexibility technologies and strategies. M&V methods for EE and DR have been developed for many years and are evolving toward increased use of automation and hourly meter data (e.g., "advanced M&V" or "M&V 2.0" with and without control groups). Similarly, hourly data, and in some cases sub-hourly data, and advanced telemetry are needed for demand flexibility market settlement. These metrics along with new and scalable evaluation methods must also be developed for the full complement of grid services that buildings can provide. Simplified approaches are needed for demand flexibility performance assessments at the whole building and system/equipment level and for multiple demand flexibility modes (e.g., shed and shift in combination). landmark    Expand EE benchmark dataset and benchmarking tools to incorporate demand flexibility. There is a long practice of collecting total energy use normalized by floor area to compare the energy performance of buildings...