LAPSE:2023.26171
Published Article
LAPSE:2023.26171
Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method
March 31, 2023
Over the years, electricity consumption behavior in Brazil has been analyzed due to financial and social problems. In this context, it is important to simulate energy prices of the energy efficiency auctions in the Brazilian electricity market. The Markov Chain Monte Carlo (MCMC) method generated simulations; thus, several samples were generated with different sizes. It is possible to say that the larger the sample, the better the approximation to the original data. Then, the Kernel method and the Gaussian mixture model used to estimate the density distribution of energy price, and the MCMC method were crucial in providing approximations of the original data and clearly analyzing its impact. Next, the behavior of the data in each histogram was observed with 500, 1000, 5000 and 10,000 samples, considering only one scenario. The sample which best approximates the original data in accordance with the generated histograms is the 10,000th sample, which consistently follows the behavior of the data. Therefore, this paper presents an approach to generate samples of auction energy prices in the energy efficiency market, using the MCMC method through the Metropolis−Hastings algorithm. The results show that this approach can be used to generate energy price samples.
Keywords
demand side bidding, Energy, Energy Efficiency, Gaussian mixture model, MCMC
Suggested Citation
López-Gonzales JL, Castro Souza R, Leite Coelho da Silva F, Carbo-Bustinza N, Ibacache-Pulgar G, Calili RF. Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method. (2023). LAPSE:2023.26171
Author Affiliations
López-Gonzales JL: Facultad de Ingeniería y Arquitectura, Universidad Peruana Unión, Lima 15, Peru; Instituto de Estadística, Universidad de Valparaíso, Valparaíso 2360102, Chile [ORCID]
Castro Souza R: Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil [ORCID]
Leite Coelho da Silva F: Mathematics Department, Federal Rural University of Rio de Janeiro, Seropédica 23897-000, Brazil [ORCID]
Carbo-Bustinza N: Doctorado Interdisciplinario en Ciencias Ambientales, Universidad de Playa Ancha, Valparaíso 2340000, Chile [ORCID]
Ibacache-Pulgar G: Instituto de Estadística, Universidad de Valparaíso, Valparaíso 2360102, Chile [ORCID]
Calili RF: Postgraduate Program in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil [ORCID]
Journal Name
Energies
Volume
13
Issue
17
Article Number
E4544
Year
2020
Publication Date
2020-09-02
Published Version
ISSN
1996-1073
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Original Submission
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PII: en13174544, Publication Type: Journal Article
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LAPSE:2023.26171
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doi:10.3390/en13174544
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