Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method

Javier Linkolk López-Gonzales, Reinaldo Castro Souza, Felipe Leite Coelho da Silva, Natalí Carbo-Bustinza, Germán Ibacache-Pulgar and Rodrigo Flora Calili
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Javier Linkolk López-Gonzales: Facultad de Ingeniería y Arquitectura, Universidad Peruana Unión, Lima 15, Peru
Reinaldo Castro Souza: Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil
Felipe Leite Coelho da Silva: Mathematics Department, Federal Rural University of Rio de Janeiro, Seropédica 23897-000, Brazil
Natalí Carbo-Bustinza: Doctorado Interdisciplinario en Ciencias Ambientales, Universidad de Playa Ancha, Valparaíso 2340000, Chile
Germán Ibacache-Pulgar: Instituto de Estadística, Universidad de Valparaíso, Valparaíso 2360102, Chile
Rodrigo Flora Calili: Postgraduate Program in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil

Energies, 2020, vol. 13, issue 17, 1-19

Abstract: 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; MCMC; energy; energy efficiency; Gaussian mixture model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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