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About

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
002
Publications

2021

Optimal HVAC System Operation Using Online Learning of Interconnected Neural Networks

Authors
Jang, YE; Kim, YJ; Catalao, JPS;

Publication
IEEE Transactions on Smart Grid

Abstract
Optimizing the operation of heating, ventilation, and air-conditioning (HVAC) systems is a challenging task that requires the modeling of complex nonlinear relationships among the HVAC load, indoor temperature, and outdoor environment. This paper proposes a new strategy for optimal operation of an HVAC system in a commercial building. The system for indoor temperature control is divided into three sub-systems, each of which is modeled using an artificial neural network (ANN). The ANNs are then interconnected and integrated into an optimization problem for temperature set-point scheduling. The problem is reformulated to determine the optimal set-points using a deterministic search algorithm. After the optimal scheduling has been initiated, the ANNs undergo online learning repeatedly, mitigating overfitting. Case studies are conducted to analyze the performance of the proposed strategy, compared to strategies with a pre-determined temperature set-point, an ideal physics-based building model, and other types of machine learning-based modeling and scheduling methods. The case study results confirm that the proposed strategy is effective in terms of the HVAC energy cost, practical applicability, and training data requirements. IEEE

2021

Optimal placement of battery swap stations in microgrids with micro pumped hydro storage systems, photovoltaic, wind and geothermal distributed generators

Authors
Rezaee Jordehi, A; Javadi, MS; P. S. Catalão, J;

Publication
International Journal of Electrical Power and Energy Systems

Abstract
The penetration of electric vehicles (EVs) in vehicle markets is increasing; however long charging time in battery charging stations is an obstacle for larger adoption of EVs. In order to address this problem, battery swap stations (BSSs) have been introduced to exchange near-empty EV batteries with fully charged batteries. Refilling an EV in BSS takes only a few minutes. With decentralization of power systems, BSSs are typically connected to the microgrid (MG) in their neighborhood. Although the location of BSS in MG affects MG operation cost, to the best knowledge of the author, optimal placement of BSS has not been done from the perspective of MG. Therefore, in this paper, the objective is to find optimal location of BSSs in a MG with micro pumped hydro storage (PHS), photovoltaic, wind and geothermal units, while reactive power dispatch and all network constraints are considered by AC optimal power flow. The effect of BSS capacity and maximum charging/discharging power, BSS to MG link capacity, PHS capacity and maximum power of PHS unit on MG operation and optimal BSS location are investigated. DICOPT solver in general algebraic mathematical system (GAMS) is used to solve the formulated mixed-integer nonlinear optimisation problem. © 2020 Elsevier Ltd

2021

Optimal scheduling of an EV aggregator for demand response considering triple level benefits of three-parties

Authors
Ren, H; Zhang, AW; Wang, F; Yan, XH; Li, Y; Duic, N; Shafie khah, M; Catalao, JPS;

Publication
International Journal of Electrical Power and Energy Systems

Abstract
The electric vehicle (EV), when aggregated by an agent (Aggregator), is a suitable candidate for participating in demand response in power system operation. As the interface between distribution network and EV users, as well as an independent party at the same time, an optimal scheduling algorithm is necessary with consideration of benefits of three parties, which in return will affect aggregators’ sustainable development. The benefits of distribution system from demand response, aggregator and EV users are defined in this paper. EV users’ benefit is described by their satisfaction on SOCs reached after a given period of time and overall costs/revenues for charging/discharging and policy award/penalty, while the benefit of distribution network for the integration of large amount EV loads through aggregator is evaluated by aggregator's load shifting capability through a price-based demand response (DR) program under real time electricity price. The optimal scheduling of the aggregator is with an objective of maximizing its own benefit under constraints of EV users’ minimum satisfaction and minimum load-shifting capability required by distribution network. The optimization scheduling is tested by a test system, and further analysis is given on the effect of aggregator's facility level and technology (Vehicle to Vehicle) and the operation mode of aggregator group on the benefits of three parties. © 2020 Elsevier Ltd

2021

Risk-Averse Optimal Energy and Reserve Scheduling for Virtual Power Plants Incorporating Demand Response Programs

Authors
Vahedipour Dahraie, M; Rashidizadeh Kermani, H; Shafie Khah, M; Catalao, JPS;

Publication
IEEE Trans. Smart Grid

Abstract

2021

Optimal Peak Shaving Control Using Dynamic Demand and Feed-In Limits for Grid-Connected PV Sources With Batteries

Authors
Manojkumar, R; Kumar, C; Ganguly, S; Catalao, JPS;

Publication
IEEE Systems Journal

Abstract
Peak shaving of utility grid power is an important application, which benefits both grid operators and end users. In this article, an optimal rule-based peak shaving control strategy with dynamic demand and feed-in limits is proposed for grid-connected photovoltaic (PV) systems with battery energy storage systems. A method to determine demand and feed-in limits depending on the day-ahead predictions of load demand and PV power profiles is developed. Furthermore, an optimal rule-based control strategy that determines day-ahead charge/discharge schedules of battery for peak shaving of utility grid power is proposed. The rules are formulated such that the peak utility grid demand and feed-in powers are limited to the corresponding demand and feed-in limits of the day, respectively, while ensuring that the state-of-charge (SoC) of the battery at the end of the day is the same as the SoC of the start of the day. The optimal inputs required for applying the proposed rule-based control strategy are determined using a genetic algorithm for minimizing peak energy drawn from the utility grid. The proposed control algorithm is tested for various PV power and load demand profiles using MATLAB. IEEE

Supervised
thesis

2020

Os Desafios da Mobilidade Elétrica em Portugal num Contexto de Cidades Inteligentes

Author
Manuel João Martins Fraga

Institution
UP-FEUP

2020

Novel Strategies to Promote Consumer Engagement in Local Energy Markets

Author
Matthew Brian Gough

Institution
UP-FEUP

2020

Coordinated operation of Electric Vehicle Solar Parking Lot as a Virtual Power Plant

Author
Tiago Poças de Almeida

Institution
UP-FEUP

2020

Estratégias de Licitação de Produtores Renováveis no Mercado Ibérico de Eletricidade

Author
José Gabriel Teixeira Pinto Xavier de Oliveira

Institution
UP-FEUP

2020

Developing demand response tools in the multi-energy systems environment

Author
Morteza Vahid Ghavidel

Institution
UP-FEUP