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

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  • Javier Linkolk López-Gonzales

    (Facultad de Ingeniería y Arquitectura, Universidad Peruana Unión, Lima 15, Peru
    Instituto de Estadística, Universidad de Valparaíso, Valparaíso 2360102, Chile)

  • 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)

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.

Suggested Citation

  • Javier Linkolk López-Gonzales & Reinaldo Castro Souza & Felipe Leite Coelho da Silva & Natalí Carbo-Bustinza & Germán Ibacache-Pulgar & Rodrigo Flora Calili, 2020. "Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method," Energies, MDPI, vol. 13(17), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4544-:d:407659
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    Cited by:

    1. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
    2. Hasnain Iftikhar & Aimel Zafar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models," Mathematics, MDPI, vol. 11(16), pages 1-19, August.
    3. Hasnain Iftikhar & Nadeela Bibi & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan," Energies, MDPI, vol. 16(6), pages 1-17, March.
    4. Felipe Leite Coelho da Silva & Kleyton da Costa & Paulo Canas Rodrigues & Rodrigo Salas & Javier Linkolk López-Gonzales, 2022. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector," Energies, MDPI, vol. 15(2), pages 1-12, January.
    5. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method," Energies, MDPI, vol. 16(18), pages 1-22, September.

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