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About

I hold a degree in Mechatronics Engineering from the German University in Cairo, Egypt, and a degree in Computational Fluid Mechanics from FEUP, Porto, Portugal. Currently, I am part of the MIT Portugal Program in Sustainable Energy Systems, being also a Research Assistant at INESC TEC. My current research interests include Demand Response, Decentralized Energy Markets, Cyber-Physical Systems, and Numerical Modeling and Simulation.

As of August 2019, I have authored and co-authored more than 35 peer-reviewed publications including 5 journal papers and 26 conference proceedings papers, in addition to several book chapters and technical reports.

I have been the Publications Chair and Web Coordinator of the SEST (International Conference on Smart Energy Systems and Technologies) series since 2018.

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Publications

2020

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

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

Publication
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

Multi-objective optimisation method for coordinating battery storage systems, photovoltaic inverters and tap changers

Authors
Hashemipour, N; Aghaei, J; Lotfi, M; Niknam, T; Askarpour, M; Shafie khah, M; Catalao, JPS;

Publication
IET Renewable Power Generation

Abstract
The many well-established advantages of distributed generation (DG) make their usage in active distribution networks prevalent. However, uncontrolled operation of DG units can negatively interfere with the performance of other equipment, such as tap-changers, in addition to resulting in sub-optimal usage of their potential. Thus, adequate scheduling/ control of DG units is critical for operators of the distribution system to avoid those adverse effects. A linearised model of a multi-objective method for coordinating the operation of photovoltaics, battery storage systems, and tap-changers is proposed. Three objective functions are defined for simultaneously enhancing voltage profile, minimising power losses, and reducing peak load power. The formulated multi-objective problem is solved by means of the epsilon-constraint technique. A novel decision-making methodology is offered to find the Pareto optimality and select the preferred solution. To assess to proposed model's performance, it is tested using 33-bus IEEE test system. Consequently, tap-changers suffer lessened stress, the batteries state-of-charge is kept within adequate limits, and the DG units operation is at higher efficiency. The obtained results verify the effectiveness of this approach. © The Institution of Engineering and Technology 2019

2020

Improved EMD-Based Complex Prediction Model for Wind Power Forecasting

Authors
Abedinia, O; Lotfi, M; Bagheri, M; Sobhani, B; Shafie khah, M; Catalao, JPS;

Publication
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY

Abstract
As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this article proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Several error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.

2019

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

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

Publication
Renewable and Sustainable Energy Reviews

Abstract

2019

Demand-Side management of smart distribution grids incorporating renewable energy sources

Authors
Osorio, GJ; Shafie khah, M; Lotfi, M; Ferreira Silva, BJM; Catalao, JPS;

Publication
Energies

Abstract
The integration of renewable energy resources (RES) (such as wind and photovoltaic (PV)) on large or small scales, in addition to small generation units, and individual producers, has led to a large variation in energy production, adding uncertainty to power systems (PS) due to the inherent stochasticity of natural resources. The implementation of demand-side management (DSM) in distribution grids (DGs), enabled by intelligent electrical devices and advanced communication infrastructures, ensures safer and more economical operation, giving more flexibility to the intelligent smart grid (SG), and consequently reducing pollutant emissions. Consumers play an active and key role in modern SG as small producers, using RES or through participation in demand response (DR) programs. In this work, the proposed DSM model follows a two-stage stochastic approach to deal with uncertainties associated with RES (wind and PV) together with demand response aggregators (DRA). Three types of DR strategies offered to consumers are compared. Nine test cases are modeled, simulated, and compared in order to analyze the effects of the different DR strategies. The purpose of this work is to minimize DG operating costs from the Distribution System Operator (DSO) point-of-view, through the analysis of different levels of DRA presence, DR strategies, and price variations. © 2019 by the authors.