Use of MCMC to incorporate a wind power model for the evaluation of generating capacity adequacy

Almutairi, Abdulaziz; Ahmed, Mohammed Hassan; Salama, M.M.A.

doi:10.1016/j.epsr.2015.12.015

Cited by 31 publications

(27 citation statements)

References 23 publications

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“…MCMC is a widely used technique and is considered a mainstream statistical tool. It is used in real estate market prediction (41), earthquake and rock fracturing (42), electricity capacity modeling (43), weather prediction (44), betting (45), climate (46), computational biology (47), computational linguistics (48), genetics (49), engineering (50), physics (51), aeronautics (52), stock market prediction (53), and social science (54). The key papers describing the algorithms used within the MCMC are Metropolis et al (55), with 37,506 cites in Google Scholar (as at May 27, 2018), and Hastings (56), with 12,229 cites providing some measure of their widespread acceptance and use.…”

Section: The General Acceptance Testmentioning

confidence: 99%

The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity

Buckleton

Bright

Gittelson³

et al. 2018

Journal of Forensic Sciences

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Forensic DNA interpretation is transitioning from manual interpretation based usually on binary decision-making toward computer-based systems that model the probability of the profile given different explanations for it, termed probabilistic genotyping (PG). Decision-making by laboratories to implement probability-based interpretation should be based on scientific principles for validity and information that supports its utility, such as criteria to support admissibility. The principles behind STRmix™ are outlined in this study and include standard mathematics and modeling of peak heights and variability in those heights. All PG methods generate a likelihood ratio (LR) and require the formulation of propositions. Principles underpinning formulations of propositions include the identification of reasonably assumed contributors. Substantial data have been produced that support precision, error rate, and reliability of PG, and in particular, STRmix™. A current issue is access to the code and quality processes used while coding. There are substantial data that describe the performance, strengths, and limitations of STRmix™, one of the available PG software.

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Section: The General Acceptance Testmentioning

confidence: 99%

The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity

Buckleton

Bright

Gittelson³

et al. 2018

Journal of Forensic Sciences

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“…Isto posto, pode-se destacar duas grandes problemáticas a serem tratadas no contexto do modelo NEWAVE, a primeira delas é referente ao processo de otimização e a segunda refere-se ao modelo estocástico utilizado para geração de séries sintéticas de Energia Afluente, tema no qual essa tese se insere. (RIBEIRO, et al, 2016), (Souza, et al, 2014), , Sobre os desenvolvimentos apresentados destacam-se os trabalhos de (PAPAEFTHYMIOU & KLÖCKL, 2008), que deu base para (Almutairi, et al, 2016), onde os autores tratam da simulação de potência eólica, porém utiliza-se o MCMC de forma levemente diferente do padrão, nestes casos é definida a matriz de transição e simulam-se, a partir da técnica Monte Carlo, os cenários de interesse.…”

Section: Quantidadeunclassified

“…Dessa forma, tal premissa é aplicada no modelo proposto nessa tese, em um contexto de Energia Natural Afluente. Sendo assim, para simular os dados de ENA, altera-se a forma de calcular a matriz de transição periódica, fazendo com que a mesma seja intercorrelacionada com os meses anteriores, diferentemente do trabalho de (Almutairi, et al, 2016), que considera as transições independentes.…”

Section: A Questão Das Interconfiguraçõesunclassified

“…Em (Penna, et al, 2011) Destarte, mais especificamente dos trabalhos de (Penna, et al, 2011), (Löhndorf & Shapiro, 2017) e (Almutairi, et al, 2016)…”

Section: A Questão Das Interconfiguraçõesunclassified

“…Optou-se por gerar uma cadeia de Markov periódica e discreta para melhorar a representação das distribuições de probabilidade e as correlações da série temporal como apresentado em (Almutairi, et al, 2016). Porém a grande diferença, e a principal inovação da metodologia proposta, é o cálculo das matrizes de transição das cadeias de Markov intercorrelacionadas.…”

Section: Mcmc Interconfiguraçõesunclassified

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Markov Chain Monte Carlo Para Simulação De Cenários De Energia Natural Afluente

BALDIOTI¹

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Consisting of an electro-energetic matrix with hydro predominance and a continental proportion territory, Brazil presents unique characteristics, being able to make use of the abundant water resources in the national territory. Approximately 65% of the electricity generation capacity comes from hydropower while 28% from thermoelectric plants. It is known that hydrological regimes have a stochastic nature and it is necessary to treat them so the energy system can be planned, thus the hydrothermal dispatch is extremely important and characterized by its stochastic dependence. From the natural streamflows it is possible to calculate the Natural Inflow Energy (NIE) that will be used directly in the synthetic series simulation process, which, in turn, are used on the optimization process, responsible for optimal policy calculation in order to minimize the system operational costs. The studies concerning the simulation of synthetic scenarios of NIE have been developing with new methodological proposals over the years. Such developments often presuppose data Gaussianity, so that a parametric distribution can be fitted to them. It was noticed that in the majority of real cases, in the context of the Brazilian Electrical Sector, the data cannot be treated like that, since they present in their density relevant tail behavior and skewness. It is necessary for the National Interconnected System (SIN) operational planning that the intrinsic skewness behavior is amenable to reproduction. Thus, this paper proposes two non-parametric approaches to scenarios simulation. The first one refers to the process of NIE series residues sampling, using a Markov Chain Monte Carlo (MCMC) technique and the Kernel Density Estimation. The second methodology is also proposed where the MCMC is applied periodically and directly in the NIE series to simulate synthetic scenarios using an innovative approach for transitions between matrices. The methodologies implementation results, observed graphically and based on statistical tests of adherence to the historical data, indicate that the proposals can reproduce with greater accuracy the asymmetric characteristics without losing the PUC-Rio-Certificação Digital Nº 1421641/CA ability to reproduce basic statistics. Thus, one can conclude that the proposed models are good alternatives in relation to the current model of the Brazilian Electric Sector.

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