Electricity market clearing algorithms: A case study of the Bulgarian power system

Koltsaklis, Nikolaos E.; Dagoumas, Athanasios; Mladenov, Valeri

doi:10.1080/15567249.2020.1845252

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…Compared with SVM, AdaBoost algorithm belongs to supervised learning, and the output index has a clear classification label, which reduces the uncertainty of the system. In order to obtain higher power demand prediction accuracy, [23] compared the weak classifier weight-setting methods of static weight and dynamic weight. In order to improve the adaptability of the classifier, the weight is adjusted according to the similarity between the predicted output and the real output during each iteration.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed Power Trading System Based on Blockchain Technology

2021

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The power trading system has the characteristics of nonlinearity, dynamics, and complexity. Part of the business data in the trading system needs to be exposed to numerous external business systems. The traditional centralized power trading model has some problems, such as low data security and trust crisis of regulators. Blockchain technology provides prominent ideas for solving these problems. Firstly, the improved AdaBoost algorithm is used to predict the supply and demand gap of power trading nodes. Secondly, based on the fact that the information on the blockchain is only open to the power supply side, a two-stage game model of asymmetric information between the power supply side and the power user is constructed to capture the price competition behavior between them in order to find the Nash equilibrium price in two stages. Finally, the US PJM market electricity market data are used to carry out an example analysis to verify the effectiveness of the algorithm and model.

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Section: Literature Reviewmentioning

confidence: 99%