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Sobre

Sobre

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.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Catalão
  • Cluster

    Energia
  • Cargo

    Investigador Coordenador
  • Desde

    01 março 2016
002
Publicações

2022

Optimal Energy Management of a Residential Prosumer: A Robust Data-Driven Dynamic Programming Approach

Autores
Guo, ZJ; Wei, W; Chen, LJ; Wang, ZJ; Catalao, JPS; Mei, SW;

Publicação
IEEE SYSTEMS JOURNAL

Abstract
Prosumers are agents that both consume and produce energy. This article studies the optimal energy management of a residential prosumer which consists of a renewable power plant and an energy storage unit. Energy could stream among power grid, renewable plant, storage unit, and demand, providing a highly flexible energy supply and the opportunity of arbitrage. To capture the uncertainty of renewable generation and electricity price, as well as the rolling horizon feature of the multiperiod energy management, the problem is formulated as a robust data-driven dynamic programming (RDDP). Kernel regression is utilized to build the empirical conditional distribution in a data-driven manner, and all candidates that reside in a Wasserstein metric-based ambiguity set are taken into account to tackle the inexactness of the empirical distribution. The RDDP can be transformed into a series of convex optimization problems with cost-to-go functions in their constraints. The piecewise linear expression of the cost-to-go function is retrieved from dual linear programs. Through such an analytical expression of cost-to-go functions, the RDDP can be solved via backward induction, unlike the popular stochastic dual dynamic programming technique that incorporates forward and backward passes. Case studies validate the performance and advantage of the proposed RDDP approach. IEEE

2022

Enhancing Transient Stability of Distribution Networks With Massive Proliferation of Converter-Interfaced Distributed Generators

Autores
Tajdinian, M; Jahromi, MZ; Hemmatpour, MH; Dehghanian, P; Shafie-khah, M; Catalao, JPS;

Publicação
IEEE SYSTEMS JOURNAL

Abstract
High penetration of renewable energy sources and energy storage systems has considerably increased the flexibility in power distribution networks operation. However, employing converter-interfaced energy and storage sources may significantly reduce the mechanical inertia and as a result, the power grids may confront serious stability challenges during transient conditions. This article introduces a strategy for enhancing transient stability margin of active distribution networks with high penetration of electric vehicles (EVs). The proposed optimization strategy intends to control EVs contributions during transient stability conditions. The EVs contributions are controlled through a new index proposed based on the system's total corrected critical kinetic energy (TCCKE). The proposed procedure for TCCKE calculation is driven by a hybrid algorithm taking into account the equal area criterion and sensitivity analysis. The suggested procedure for TCCKE only depends on the during fault data and as a result, the proposed optimization strategy is useful to prevent transient instability in the case of first swing instability. The proposed optimization is applied and evaluated on the IEEE test systems. The results clearly demonstrate the applicability and efficacy during a multitude of fault and emergency conditions. IEEE

2022

Closed loop Aggregated Baseline Load Estimation using Contextual Bandit with Policy Gradient

Autores
Zhang, YF; Wu, QW; Ai, Q; Catalao, JPS;

Publicação
IEEE TRANSACTIONS ON SMART GRID

Abstract
Demand response (DR) is an important technique to explore the demand-side flexibility. The wide deployment of smart meters makes it possible to quantify the baseline load. As an intermediate agent, demand response aggregator needs to obtain the aggregated baseline load (ABL) for the DR event. Previous studies about the household level estimation focus on the estimation method. However, for ABL estimation, customer division is an important issue. A major limitation is the mismatch between the objectives of segmentation and estimation. Therefore, this paper proposes a new closed-loop method for estimating the ABL, which utilizes the contextual bandit with policy gradient to link the segmentation with the estimation. As such, the ABL estimation accuracy can guide the segmentation to divide the customers. The segmentation and estimation optimize collaboratively to improve the ABL estimation accuracy. An ensemble method for combining network’s weights during the training process is proposed. Moreover, a pre-and post-event adjustment method is developed to further improve the estimation accuracy. Comprehensive comparisons demonstrate the proposed method can achieve the best estimation performance with regard to the MAPE and RMSE. It improves the estimation accuracy by 7% in terms of MAPE, and 11% in terms of RMSE.

2022

Experimentally Validated Extended Kalman Filter Approach for Geomagnetically Induced Currents Measurement

Autores
Behdani, B; Tajdinian, M; Allahbakhshi, M; Popov, M; Shafie khah, M; Catalao, JPS;

Publicação
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Abstract
Geomagnetically induced currents (GICs) are referred to the quasi-DC current flows in power networks, driven by complex space weather-related phenomena. Such currents are a potential threat to the power delivery capability of electrical grids. To mitigate the detrimental impacts of GICs on critical infrastructures, the GICs should be monitored in power systems. Being inherently DC from the power frequency point of view, the components of GICs are, however, challenging and costly to monitor in AC power grids. This paper puts forward a novel methodology for the real-time estimation of GICs in power transformers. Such aim is attained by means of an extended Kalman filter (EKF)-based approach, mounted on the nonlinear state-space model of the transformer, whose parameters can be derived from standard tests. The proposed EKF-based algorithm employs the available measurements for the transformer differential protection. The proposed approach, relying on the differential current, can properly deal with the external sources of interference like harmonic excitation and loading. The EKF-based estimator presented is validated by simulation and experimental data. The results verify the ability of the proposed approach to robustly estimate the GIC level during various operating conditions.

2022

A Machine Learning-based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

Autores
Li, S; Ding, T; Jia, WH; Huang, C; Catalao, JPS; Li, FX;

Publicação
IEEE TRANSACTIONS ON POWER SYSTEMS

Abstract
This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

Teses
supervisionadas

2021

High Power Fiber Laser for Space Applications

Autor
Fernando Augusto Ortigão Festas Maia

Instituição
UM

2021

Performance of Smart Homes for participating in Electricity Markets

Autor
Pedro do Couto Reis e Silva

Instituição
UP-FEUP

2021

Decentralized Cloud-Based Approaches for Cross-Sector Demand Side Management

Autor
Mohamed Fouad Hassan Lotfi Mahmoud

Instituição
UP-FEUP

2021

Flexibility Provision by Active Prosumers in Islanded Micro-Grids

Autor
Rodrigo Miguel Castro Lopes Melo Vieira

Instituição
UP-FEUP

2021

Agent Based Modeling of Peer-to-PeerTransactions in a Smart Grid Environment

Autor
Diogo Veiga Guimarães

Instituição
UP-FEUP