Abstract
Uncertainty is ubiquitous in power systems, and it will probably harm the stability of power systems. However, the uncertainty should be quantized to better understand and avoid its negative effect. To quantize the uncertainty, electricity transactions are selected as the main scenario for further research, because the uncertainty is highly relevant to the economic value of market participants, which can be analytically expressed. In electricity transactions, market participants make decisions to get maximum profits with uncertainty, thus they should thoroughly evaluate the impact of uncertainty on their economic profits. To accurately model the uncertainty, the distributions of random variables are used to depict it, and kernel density estimation (KDE) is used in the distribution fitting for enhancing the accuracy. Then, six kernel functions are tested in distribution fitting, and the data value rates related to these candidate kernel functions are derived to measure the positive impact of the uncertainty reduction on market participants’ economic profits. Finally, case studies are employed with practical smart meter data and electricity market price data. Through case studies, the theoretical results that we derive are validated and the instructions for using kernel density estimation in the data valuation with uncertainty are proposed.
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This work was supported in part by the National Key Research and Development Program of China 2020YFB2104500.
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Wang, B., Xia, T., Guo, Q. (2023). Data Valuation in Electricity Transactions Incorporating Uncertainty Reduction by Kernel Density Estimation. In: Zeng, P., Zhang, XP., Terzija, V., Ding, Y., Luo, Y. (eds) The 37th Annual Conference on Power System and Automation in Chinese Universities (CUS-EPSA). CUS-EPSA 2022. Lecture Notes in Electrical Engineering, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-99-1439-5_14
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DOI: https://doi.org/10.1007/978-981-99-1439-5_14
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