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

Energies

Reinaldo Castro Souza¹  Natalí Carbo-Bustinza²  JavierLinkolk López-Gonzales³  Germán Ibacache-Pulgar⁴  Felipe Leite Coelho da Silva⁵  RodrigoFlora Calili⁶ 

[1] Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil;Doctorado Interdisciplinario en Ciencias Ambientales, Universidad de Playa Ancha, Valparaíso 2340000, Chile;Facultad de Ingeniería y Arquitectura, Universidad Peruana Unión, Lima 15, Peru;Instituto de Estadística, Universidad de Valparaíso, Valparaíso 2360102, Chile;Mathematics Department, Federal Rural University of Rio de Janeiro, Seropédica 23897-000, Brazil;Postgraduate Program in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil;

关键词: demand side bidding;    MCMC;    energy;    energy efficiency;    Gaussian mixture model;   

DOI  :  10.3390/en13174544

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown