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About

J. T. Saraiva was born in Porto, Portugal, in 1962 and got his MSc, PhD, and Agregado degrees in Electrical and Computer Engineering from FEUP in 1987, 1993 and 2002, where he is currently Professor. In 1985 he joined INESC Porto where he is head researcher and collaborated in several EU financed projects, in national funded projects and in several consultancy contracts with the Portuguese Electricity Regulatory Agency, with EDP Distribuição, EDP Produção, REN, Empresa de Electricidade da Madeira, Empresa de Electricidade dos Açores and with the Greek and the Brasilean Transmission System Operators. Along his Academic career he supervized more than 50 MSc Thesis and 10 PhD Thesis, co authored 3 books, more than 30 papers in international journals and more than 120 papers in International Conferences.

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Details

Details

  • Name

    João Tomé Saraiva
  • Cluster

    Power and Energy
  • Role

    Research Coordinator
  • Since

    15th July 1985
019
Publications

2020

A two-stage strategy for security-constrained AC dynamic transmission expansion planning

Authors
Gomes, PV; Saraiva, JT;

Publication
Electric Power Systems Research

Abstract
This paper presents a new and promising strategy organized in two stages to solve the dynamic multiyear transmission expansion planning, TEP, problem. Specifically, the first stage is related to the reduction of the search space size and it is conducted by a novel constructive heuristic algorithm (CHA). The second one is responsible for the refinement of the optimal solution plan and it uses a novel evolutionary algorithm based on the best features of particle swarm optimization (PSO) and genetic algorithm (GA). The planning problem is modelled as a dynamic and multiyear approach to ensure that it keeps a holistic view over the entire planning horizon and it aims at minimizing the total system costs comprising the investment and operation costs. Additionally, the N-1 contingency criterion is also considered in the problem. The developed approach was tested using the IEEE 118-Bus test system and the obtained results demonstrate its advantages in terms of efficiency and required computational time. Furthermore, the results demonstrated that the novel strategy can enable the utilization of the AC optimal power flow (OPF) in a faster and reliable way when compared to the standard and widespread DC-OPF model. © 2019 Elsevier B.V.

2019

State-of-the-art of transmission expansion planning: A survey from restructuring to renewable and distributed electricity markets

Authors
Gomes, PV; Saraiva, JT;

Publication
International Journal of Electrical Power and Energy Systems

Abstract
Transmission Expansion Planning (TEP) problem aims at identifying when and where new equipment as transmission lines, cables and transformers should be inserted on the grid. The transmission upgrade capacity is motivated by several factors as meeting the increasing electricity demand, increasing the reliability of the system and providing non-discriminatory access to cheap generation for consumers. However, TEP problems have been changing over the years as the electrical system evolves. In this way, this paper provides a detailed historical analysis of the evolution of the TEP over the years and the prospects for this challenging task. Furthermore, this study presents an outline review of more than 140 recent articles about TEP problems, literature insights and identified gaps as a critical thinking in how new tools and approaches on TEP can contribute for the new era of renewable and distributed electricity markets. © 2019 Elsevier Ltd

2019

Impact of decision-making models in Transmission Expansion Planning considering large shares of renewable energy sources

Authors
Gomes, PV; Saraiva, JT; Carvalho, L; Dias, B; Oliveira, LW;

Publication
Electric Power Systems Research

Abstract

2019

On the use of causality inference in designing tariffs to implement more effective behavioral demand response programs

Authors
Ganesan, K; Saraiva, JT; Bessa, RJ;

Publication
Energies

Abstract
Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers’ usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers’ elasticity is effectively utilized. © 2019 by the authors.

2019

Using causal inference to measure residential consumers demand response elasticity

Authors
Ganesan, K; Saraiva, JT; Bessa, RJ;

Publication
2019 IEEE Milan PowerTech, PowerTech 2019

Abstract
Engaging the residential consumers and providing the best tariffs for their randomized behavior is one of the major barriers to demand response (DR) implementation. Additionally, DR offers submitted by aggregators or retailers are not consumer-specific, which turns it even more difficult for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causal inference between dynamic DR tariffs and observed residential electricity consumption (resolution of 30 minutes) to estimate consumers' consumption elasticity. Ultimately, the aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. © 2019 IEEE.

Supervised
thesis

2019

Estimativa da Valia Económica da PRE no Ano de 2017

Author
Manuel Maria de Sousa Dias Alves da Silva

Institution
UP-FEUP

2019

Simulation of Hydro Power Plants in Electricity Markets Using an Agent-Based Model

Author
José Carlos Vieira Sousa

Institution
UP-FEUP

2019

Residential Consumer Behavioural Analysis on the participation in Demand Response Strategies including distributed generation and electric vehicles

Author
Kamalanathan Ganesan

Institution
UP-FEUP

2019

Multi-objective long-term transmission expansion planning

Author
Luiz Eduardo de Oliveira

Institution
UP-FEUP

2019

Economic and Regulatory Schemes to Maximize the Social Benefit of Energy Communities

Author
Rogério Rui Dias da Rocha

Institution
UP-FEUP