Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method
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 si...
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2020
|
| Materias: | |
| Acceso en línea: | Ver en el sistema de origen Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881508861707288576 |
|---|---|
| author | López Gonzales, Javier Linkolk Castro Souza, Reinaldo Leite Coelho da Silva, Felipe Carbo‐Bustinza, Natalí Ibacache‐Pulgar, Germán Calili, Rodrigo Flora |
| author_browse | Calili, Rodrigo Flora Carbo‐Bustinza, Natalí Castro Souza, Reinaldo Ibacache‐Pulgar, Germán Leite Coelho da Silva, Felipe López Gonzales, Javier Linkolk |
| author_facet | López Gonzales, Javier Linkolk Castro Souza, Reinaldo Leite Coelho da Silva, Felipe Carbo‐Bustinza, Natalí Ibacache‐Pulgar, Germán Calili, Rodrigo Flora |
| author_sort | López Gonzales, Javier Linkolk |
| collection | Producción científica |
| description | 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/1525 |
| institution | UPeU |
| language | English |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/15252026-10-30T16:16:02ZSimulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo MethodLópez Gonzales, Javier LinkolkCastro Souza, ReinaldoLeite Coelho da Silva, FelipeCarbo‐Bustinza, NatalíIbacache‐Pulgar, GermánCalili, Rodrigo Florademand side biddingMCMCenergyenergy efficiencyGaussian mixture modelOver 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.Articlehttps://www.mdpi.com/1996-1073/13/17/4544/pdf?version=1599038303https://doi.org/10.3390/en1317454410.3390/en13174544https://hdl.handle.net/20.500.15037/1525W3081922605enEnergies1996-10732020-09-022026-06-13T13:21:55Z2026-06-13T13:21:55Z |
| spellingShingle | demand side bidding MCMC energy energy efficiency Gaussian mixture model López Gonzales, Javier Linkolk Castro Souza, Reinaldo Leite Coelho da Silva, Felipe Carbo‐Bustinza, Natalí Ibacache‐Pulgar, Germán Calili, Rodrigo Flora Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method |
| title | Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method |
| title_full | Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method |
| title_fullStr | Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method |
| title_full_unstemmed | Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method |
| title_short | Simulation of the Energy Efficiency Auction Prices via the Markov Chain Monte Carlo Method |
| title_sort | simulation of the energy efficiency auction prices via the markov chain monte carlo method |
| topic | demand side bidding MCMC energy energy efficiency Gaussian mixture model |
| topic_facet | demand side bidding MCMC energy energy efficiency Gaussian mixture model |
| url | https://www.mdpi.com/1996-1073/13/17/4544/pdf?version=1599038303 https://doi.org/10.3390/en13174544 https://hdl.handle.net/20.500.15037/1525 |
| work_keys_str_mv | AT lopezgonzalesjavierlinkolk simulationoftheenergyefficiencyauctionpricesviathemarkovchainmontecarlomethod AT castrosouzareinaldo simulationoftheenergyefficiencyauctionpricesviathemarkovchainmontecarlomethod AT leitecoelhodasilvafelipe simulationoftheenergyefficiencyauctionpricesviathemarkovchainmontecarlomethod AT carbobustinzanatali simulationoftheenergyefficiencyauctionpricesviathemarkovchainmontecarlomethod AT ibacachepulgargerman simulationoftheenergyefficiencyauctionpricesviathemarkovchainmontecarlomethod AT calilirodrigoflora simulationoftheenergyefficiencyauctionpricesviathemarkovchainmontecarlomethod |