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Detalhes

Detalhes

  • Nome

    Mohammad Javadi
  • Cluster

    Energia
  • Cargo

    Investigador Auxiliar
  • Desde

    01 junho 2019
001
Publicações

2020

A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation

Autores
Lotfi, M; Javadi, M; Osorio, GJ; Monteiro, C; Catalao, JPS;

Publicação
Energies

Abstract
A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., meteorological forecasts) and the corresponding local output (e.g., recorded power generation). Given a new case (with forecasted meteorological variables), the resulting power generation is forecasted. This is performed by calculating a KDE-based similarity index to determine a set of most similar cases from the historical dataset. Then, the outputs of the most similar cases are used to calculate an ensemble prediction. The method is tested using historical weather forecasts and recorded generation of a PV installation in Portugal. Despite only being given averaged data as input, the algorithm is shown to be capable of predicting uncertainties associated with high frequency weather variations, outperforming deterministic predictions based on solar irradiance forecasts. Moreover, the algorithm is shown to outperform a neural network (NN) in most test cases while being exceptionally faster (32 times). Given that the proposed model only relies on public locally-metered data, it is a convenient tool for DG owners/operators to effectively forecast their expected generation without depending on private/proprietary data or divulging their own.

2020

Stochastic planning and operation of energy hubs considering demand response programs using Benders decomposition approach

Autores
Mansouri, SA; Ahmarinejad, A; Ansarian, M; Javadi, MS; Catalao, JPS;

Publicação
International Journal of Electrical Power and Energy Systems

Abstract
In this paper, an integrated approach for optimal planning and operation of energy hubs is provided considering the effects of wind energy resources. Inevitable uncertainties of electrical, heating, cooling demands as well as the wind power generation are considered in this study. The proposed model is based on two-stage optimization problems and represented as a stochastic programming problem to address the effects of uncertain parameters. In order to address the uncertain parameters in the model, different scenarios have been generated by Monte-Carlo Simulation approach and then the scenarios are reduced by applying K-means method. In addition, the effects of demand response programs on the operational sub-problem are taken into account. Benders decomposing approach is adopted in this research to solve the complex model of coordinated planning and operation problem. The master problem is supposed to determine the type and capacity of hub equipment, while the operating points of these assets are the decision variables of the operational slave problem. As a result, the proposed mathematical model is expressed as a linear model solved in GAMS. The simulation results confirm that the Benders decomposition method offers extremely high levels of accuracy and power in solving this problem in the presence of uncertainties and numerous decision variables. Moreover, the convergence time is drastically decreased using Benders decomposition method. © 2020 Elsevier Ltd

2020

An Improved Double-Surface Sliding Mode Observer for Flux and Speed Estimation of Induction Motors

Autores
Mansouri, SA; Ahmarinejad, A; Javadi, MS; Heidari, R; Catalao, JPS;

Publicação
IET Electric Power Applications

Abstract

2019

Impact of distributed generation on protection and voltage regulation of distribution systems: A review

Autores
Razavi, SE; Rahimi, E; Javadi, MS; Nezhad, AE; Lotfi, M; Shafie khah, M; Catalao, JPS;

Publicação
Renewable and Sustainable Energy Reviews

Abstract

2019

Optimal Spinning Reserve Allocation in Presence of Electrical Storage and Renewable Energy Sources

Autores
Javadi, MS; Lotfi, M; Gough, M; Nezhad, AE; Santos, SF; Catalao, JPS;

Publicação
2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)

Abstract