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Highlights • This paper use coping cost approach to measure affordability to access safe water. • Monthly coping costs are INR 553 and INR 658 for piped and non-piped households. • For non-piped households, collection costs constitute 22% of the coping costs. • Coping costs are as high as 15% for the low income households. Abstract Sustainable Development Goal 6.1 is "to achieve universal and equitable access to safe and affordable drinking water for all". To measure affordability of accessing clean water, coping cost approach has been adopted, and this paper contributes to the burgeoning empirical literature on measuring affordability. The objective of this paper is to estimate coping costs related to the erratic, unsafe, and inadequate water supply in the metropolitan area of Chennai, India. Based on the data collected from 423 households, we find that households in Chennai city resort to five main types of coping behaviors: collecting, pumping, treating, storing, and purchasing. We employ the multiple regression with robust errors, to estimate the determinants of the coping costs. We obtain the mean coping costs as INR (Indian Rupee) 553, and INR 658 per month for piped and non-piped households in this sample. For non-piped households, collection costs (time costs in traveling and queuing for collecting water) constitute 22% of the coping costs, while collection costs for piped households are less 2% of the coping costs. One interesting finding is the variation of coping costs with household income—these costs are roughly 1% of income for the high income households to as high as 15% for the low income households. The results outline the need of policy intervention to enhance affordability. [ABSTRACT FROM AUTHOR]

Abstract This paper briefly discusses the present framework and status of Indian power market and guides through the possible adaptations to accommodate the growing renewable energy (RE) integration into the grid. The participation of RE generators in the wholesale energy market would provide them a platform to optimally manage their generation portfolio which would help them in compensating the high investment cost. The increased competition in the market would eventually bring down the electricity price. The objective of this paper is to identify an ideal framework and optimal bidding mechanism for RE generators (wind energy in particular) to maximize the overall capital gain (social welfare) in the Indian power market. The inputs from various foreign electricity markets having high wind energy penetration have been considered. Recent studies suggest that battery energy storage system (BESS) can play a significant role in handling risks associated with the uncertainties in wind power generation and thus can help in maximizing the revenue for wind farm owners. It can also be instrumental in active/reactive power balancing which will further help in the frequency regulation. The paper probes into the combined wind and battery energy trading, and various bidding strategies in the day-ahead market (DAM), intraday energy market, real-time balancing, and other ancillary markets. Highlights • Reviews the state of renewable energy in major electricity markets worldwide. • Overviews the present structure and status of RE in Indian power market. • Literature survey on optimal trading of wind energy and battery energy storage. • Brings forward the major challenges in large-scale wind energy trading. • Suggests framework for Indian power market to facilitate renewable integration. [ABSTRACT FROM AUTHOR]

This paper establishes a hybridized intelligent machine learning based currency exchange forecasting model using Extreme Learning Machines (ELMs) and the Jaya optimization technique. This model can very well forecast the exchange price of USD (US Dollar) to INR (Indian Rupee) and USD to EURO based on statistical measures, technical indicators and combination of both measures over a time frame varying from 1 day to 1 month ahead. The proposed ELM-Jaya model has been compared with existing optimized Neural Network and Functional Link Artificial Neural Network based predictive models. Finally, the model has been validated using various performance measures such as; MAPE, Theil's U, ARV and MAE. The comparison of different features demonstrates that the technical indicators outperform both the statistical measures and a combination of statistical measures and technical indicators in ELM-Jaya forecasting model. [ABSTRACT FROM AUTHOR]