Data analytics for statistical characterization of prices and empirical resource investment planning in Indian electricity market

A. Saranya, P. Verma, T. Vidyamani, K. Swarup
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引用次数: 1

Abstract

With the advent of technological developments, a large amount of data is being created and made available at every time instance. This work tries to leverage this data for useful interpretations and derives resource investment planning strategies. Data analytics can be a useful tool to understand the optimal locations, for resource investments in the grid. The study can be helpful in reducing the search space in an optimal resource investment planning problem. As a precursor data analytics study could help in forming policies that could complement a planning problem. India is a growing economy and the electric demand of the country is also increasing. There is a need to attract more investment in the generation side to meet the future loads. It is important to statistically characterize the electricity prices to find the potential investment regions in the grid. This paper gives an overview of the restructured power industry in India, the various market segments in existence and gives a detailed statistical analysis of the Day Ahead Electricity Market prices using Minitab and Statistics toolbox of MATLAB. It also discusses the operational issues like congestion management scheme followed and their effect on the area prices, that could help in empirical resource planning and policy making.
印度电力市场价格统计特征的数据分析与经验资源投资规划
随着技术的发展,大量的数据正在被创建并在每个时间实例中可用。这项工作试图利用这些数据进行有用的解释,并派生出资源投资规划策略。数据分析可以成为了解网格资源投资的最佳位置的有用工具。该研究有助于减少资源最优投资规划问题的搜索空间。作为先行者,数据分析研究可以帮助形成政策,以补充规划问题。印度是一个不断增长的经济体,该国的电力需求也在增加。有必要在发电方面吸引更多的投资,以满足未来的负荷。对电价进行统计表征是寻找电网潜在投资区域的重要手段。本文概述了印度重组后的电力行业,现有的各个细分市场,并使用MATLAB的Minitab和Statistics工具箱对日前电力市场价格进行了详细的统计分析。本文还讨论了交通拥堵管理方案及其对区域价格的影响等操作性问题,为经验资源规划和政策制定提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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