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This study aims to assess the technical and economic potential of concentrating solar power (CSP) generation in India. The potential of CSP systems is estimated on the basis of a detailed solar radiation and land resource assessment in 591 districts across the country. The land suitability, favorable solar resource conditions and wind power density over the vicinity have been considered key parameters for potential estimation. On the basis of a district-wise solar and land resource assessment, the technical potential of CSP systems is estimated over 1500 GW at an annual direct normal irradiance (DNI) over 1800 kWh/m 2 and wind power density (WPD) ≥150 W/m 2 after taking into accounts the viability of different CSP technologies and land suitability criteria. The economic potential of CSP is estimated at 571 GW at an annual DNI over 2000 kWh/m 2 and WPD≥150 W/m 2 in India. The technical evaluation of CSP technologies over the potential locations have been carried through System Advisor Model (SAM) Software using the Typical Meteorological Year data of Meteonorm 7.0 weather database. In near future, it is anticipated that locations with DNI values ≥1600-1800 kWh/m 2 could also become economically feasible with the development of new technologies, advancement of materials, efficient and cost-effective thermal energy storage, economy of scale, manufacturing capability along with the enhanced policy measures, etc. In the long-term, it is possible to exploit over 2700 GW solar power through CSP in India with an annual DNI ≥1600 kWh/m 2 and WPD≥150 W/m 2. The findings of this study can be used for identification of niche areas for CSP projects in India.

How can we measure the generalization of models to a variety of unseen tasks when provided with their language instructions? To facilitate progress in this goal, we introduce NATURAL-INSTRUCTIONS v2 , a benchmark of 1,600+ diverse language tasks and their expertwritten instructions. It covers 70+ distinct task types, such as tagging, in-filling, and rewriting. These tasks are collected with contributions of NLP practitioners in the community and through an iterative peer review process to ensure their quality. With this large and diverse collection of tasks, we are able to rigorously benchmark cross-task generalization of models-training on a subset of tasks and evaluating on the remaining unseen ones. For instance, we quantify generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances, and model sizes. Based on these insights, we introduce Tk-INSTRUCT, an encoder-decoder Transformer that is trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples) which outperforms existing larger models on our benchmark. We hope this benchmark facilitates future progress toward more general-purpose language understanding models. 1

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