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About

João P. S. Catalão received the M.Sc. degree from the Instituto Superior Técnico (IST), Lisbon, Portugal, in 2003, and the Ph.D. degree and Habilitation for Full Professor ("Agregação") from the University of Beira Interior (UBI), Covilha, Portugal, in 2007 and 2013, respectively. Currently, he is a Professor at the Faculty of Engineering of the University of Porto (FEUP), Porto, Portugal, and Researcher at INESC TEC, INESC-ID/IST-UL, and C-MAST/UBI. He was the Primary Coordinator of the EU-funded FP7 project SiNGULAR ("Smart and Sustainable Insular Electricity Grids Under Large-Scale Renewable Integration"), a 5.2-million-euro project involving 11 industry partners. He has authored or coauthored more than 500 publications, including 171 journal papers, 303 conference proceedings papers, 29 book chapters, and 14 technical reports, with an h-index of 30 and over 3735 citations (according to Google Scholar), having supervised more than 45 post-docs, Ph.D. and M.Sc. students. He is the Editor of the books entitled Electric Power Systems: Advanced Forecasting Techniques and Optimal Generation Scheduling and Smart and Sustainable Power Systems: Operations, Planning and Economics of Insular Electricity Grids (Boca Raton, FL, USA: CRC Press, 2012 and 2015, respectively). His research interests include power system operations and planning, hydro and thermal scheduling, wind and price forecasting, distributed renewable generation, demand response and smart grids. Prof. Catalão is an Editor of the IEEE Transactions on Smart Grid, an Editor of the IEEE Transactions on Sustainable Energy, an Editor of the IEEE Transactions on Power Systems, and an Associate Editor of the IET Renewable Power Generation. He was the Guest Editor-in-Chief for the Special Section on "Real-Time Demand Response" of the IEEE Transactions on Smart Grid, published in December 2012, and the Guest Editor-in-Chief for the Special Section on "Reserve and Flexibility for Handling Variability and Uncertainty of Renewable Generation" of the IEEE Transactions on Sustainable Energy, published in April 2016. He was the recipient of the 2011 Scientific Merit Award UBI-FE/Santander Universities and the 2012 Scientific Award UTL/Santander Totta. Also, he has won 4 Best Paper Awards at IEEE Conferences.

Interest
Topics
Details

Details

  • Name

    João Catalão
  • Cluster

    Power and Energy
  • Role

    Research Coordinator
  • Since

    01st March 2016
001
Publications

2018

Transmission switching, demand response and energy storage systems in an innovative integrated scheme for managing the uncertainty of wind power generation

Authors
Aghaei, J; Nikoobakht, A; Mardaneh, M; Shafie khah, M; Catalao, JPS;

Publication
International Journal of Electrical Power and Energy Systems

Abstract
This paper addresses the stochastic security constrained unit commitment (SSCUC) problem with flexibility resources for managing the uncertainty of wind power generation (WPG). Departing from the traditional flexibility resources such as the thermal units with fast up/down spinning reserves and transmission switching (TS), this paper explores also the use of demand response (DR) and energy storage (ES) systems in an innovative integrated scheme. The proposed scheme utilizes a stochastic optimization framework to coordinate the flexibility resources dealing with the uncertainty of WPGs and equipment failures. The stochastic optimization model is formulated as a mixed-integer linear programming (MIP), and this problem is large and computationally complex even for medium sized systems. Accordingly, we present a novel accelerating decomposition technique aimed at solving this problem and reducing the number of iterations and CPU time. Numerical simulation results on the modified 6-bus system and on large-scale power systems, i.e. IEEE 118 and 300-bus systems, clearly demonstrate the benefits of applying flexibility resources for uncertainty management and the efficacy of the proposed solution strategy for large-scale systems. © 2017 Elsevier Ltd

2018

A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators

Authors
Hajibandeh, N; Shafie Khah, M; Osorio, GJ; Aghaei, J; Catalao, JPS;

Publication
Applied Energy

Abstract
Integration of wind energy and other renewable energy resources in electrical systems create some challenges due to their uncertain and variable characteristics. In the quest for more flexibility of the electric systems, combination of these endogenous and renewable resources in accordance with strategies of Demand Response (DR) allows an increment/improvement of the demand potential, as well as a more secure, robust, sustainable and economically advantageous operation. This paper proposes a new model for integration of wind power and DR, thus optimizing supply and demand side operations through a price rule Time of Use (TOU), or incentive with Emergency DR Program (EDRP), as well as combining TOU and EDRP together. The problem is modelled using a stochastic Heuristic Multi-Objective Multi-Criteria Decision Making (HMM) method which aims to minimize operation costs and environmental emissions simultaneously, ensuring the security constraints through two-stage stochastic programming, considering various techno-economic indices such as load factor, electricity market prices, Energy Not Supplied (ENS) and Share Weighted Average Lerner Index (SWALI). Comprehensive numerical results indicate that the proposed model is entirely efficient in DR planning and power system operation. © 2017 Elsevier Ltd

2018

Strategic Behavior of Multi-Energy Players in Electricity Markets as Aggregators of Demand Side Resources using a Bi-level Approach

Authors
Yazdani Damavandi, M; Neyestani, N; Shafie khah, M; Contreras, J; Catalao, JPS;

Publication
IEEE Transactions on Power Systems

Abstract

2018

Novel probabilistic optimization model for lead-acid and vanadium redox flow batteries under real-time pricing programs

Authors
Lujano Rojas, JM; Zubi, G; Dufo Lopez, R; Bernal Agustin, JL; Catalao, JPS;

Publication
International Journal of Electrical Power and Energy Systems

Abstract
The integration of storage systems into smart grids is being widely analysed in order to increase the flexibility of the power system and its ability to accommodate a higher share of wind and solar power. The success of this process requires a comprehensive techno-economic study of the storage technology in contrast with electricity market behaviour. The focus of this work is on lead-acid and vanadium redox flow batteries. This paper presents a novel probabilistic optimization model for managing energy storage systems. The model is able to incorporate the forecasting error of electricity prices, offering with this a near-optimal control option. Using real data from the Spanish electricity market from the year 2016, the probability distribution of forecasting error is determined. The model determines electricity price uncertainty by means of Monte Carlo Simulation and includes it in the energy arbitrage problem, which is eventually solved by using an integer-coded genetic algorithm. In this way, the probability distribution of the revenue is determined with consideration of the complex behaviours of lead-acid and vanadium redox flow batteries as well as their associated operating devices such as power converters. © 2017 Elsevier Ltd

2018

Effects of PEV Traffic Flows on the Operation of Parking Lots and Charging Stations

Authors
Neyestani, N; Damavandi, MY; Chicco, G; Catalao, JPS;

Publication
IEEE Transactions on Smart Grid

Abstract
The introduction of plug-in electric vehicles (PEVs) in the electrical system is bringing various challenges. The main issue is incorporating the PEV owner’s preferences in the models. One of the main attributes representing the preference of the owners is their travel purposes, impacting on the traffic flow pattern. The PEVs’ traffic pattern defines the required charging schedule of the PEVs and consequently characterizes the operation of the charging facilities such as PEV parking lots (PLs). The deployment of resources such as PEV PL requires a detailed modeling of the factors affecting their operation. In this regard, this paper aims to model the power flow of the PEVs based on their traffic flow. Different travel types and purposes are considered for the PEVs traffic modeling. Two types of charging infrastructure (i.e., PLs and individual charging stations) are considered. The study is performed on a distribution network categorized based on the consumption patterns of the zones. IEEE

Supervised
thesis

2018

Smart operation of transformers for sustainable electric vehicles integration and model predictive control for energy monitoring and management

Author
Radu Godina

Institution
Outra

2018

Enhancing the efficiency of electricity utilization through home energy management systems within the smart grid framework

Author
Tiago Mendes

Institution
Outra

2018

Modeling a cooperation environment for flexibility enhancement in smart multi-energy industrial systems

Author
Maziar Damavandi

Institution
Outra

2018

Sustainable distribution network planning considering multi-energy systems and plug-in electric vehicles parking lots

Author
Nilufar Neyestani

Institution
Outra

2018

Planning of power distribution systems with a high penetration of renewable energy sources using stochastic optimization

Author
Sérgio Santos

Institution
Outra