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About

João Claro is Chief Executive Officer of INESC TEC and Associate Professor of Industrial Engineering and Management at Faculdade de Engenharia of the Universidade do Porto (FEUP). At INESC TEC he is also a researcher in the Centre for Innovation, Technology, and Entrepreneurship. He is affiliated with Porto Business School (PBS), where he is a member of the Academic Council, and heads the Entrepreneurship and Innovation academic area. Between 2013 and 2017, he was the National Director of the Carnegie Mellon Portugal Program, an international partnership in Information and Communication Technology (ICT) between Portuguese universities, research labs and companies, and Carnegie Mellon University (CMU), funded by Fundação para a Ciência e a Tecnologia. In 2008 he was a visiting scholar with the Engineering Systems Division at MIT. In the past 15 years, he has taught and mentored more than 150 technology commercialization teams from Portuguese universities, research labs and companies, in multiple initiatives with ANI, COTEC Portugal, FCUP, FEUP, INESC TEC, PBS, and CMU. João Claro holds a Ph.D. in Electrical and Computer Engineering from FEUP (2008), an MSc in Quantitative Methods in Management from PBS (2002), and an undergraduate degree in Electrical and Computer Engineering from FEUP (1993). Prior to returning to the University, he was a software engineer and information systems project manager at Edinfor (1994-1998).

Interest
Topics
Details

Details

015
Publications

2019

Barriers to onshore wind farm implementation in Brazil

Authors
Farkat Diogenes, JRF; Claro, J; Rodrigues, JC;

Publication
Energy Policy

Abstract

2019

Coordinating cross-border electricity interconnection investments and trade in market coupled regions

Authors
Loureiro, MV; Claro, J; Fischbeck, P;

Publication
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Abstract
Investments in cross-border electricity interconnections are key for the integration of the European energy market. To analyze policy frameworks for these decisions, we model two settings for the expansion of transmission capacity between two regions, where the volume of investment is agreed upon through either Nash-Coase or Nash bargaining. For each setting we provide fair share cost allocation solutions, respectively with and without compensations. Each region has its own TSO, maximizing social welfare within its geography, and the markets are modeled with linear supply and demand curves, with trade enabled by the interconnection. The results of the application of the models to the Iberian market suggest their ability to estimate realistic values for the capacity of cross-border interconnection between two regions.

2018

Operational flexibility in forest fire prevention and suppression: a spatially explicit intra-annual optimization analysis, considering prevention, (pre)suppression, and escape costs

Authors
Pacheco, AP; Claro, J;

Publication
EUROPEAN JOURNAL OF FOREST RESEARCH

Abstract
Increasing wildfire threats and costs escalate the complexity of forest fire management challenges, which is grounded in complex interactions between ecological, social, economic, and policy factors. It is immersed in this difficult context that decision-makers must settle on an investment mix within a portfolio of available options, subject to limited funds and under great uncertainty. We model intra-annual fire management as a problem of multistage capacity investment in a portfolio of management resources, enabling fuel treatments and fire preparedness. We consider wildfires as the demand, with uncertainty in the severity of the fire season and in the occurrence, time, place, and severity of specific fires. We focus our analysis on the influence of changes in the volatility of wildfires and in the costs of escaped wildfires, on the postponement of capacity investment along the year, on the optimal budget, and on the investment mix. Using a hypothetical test landscape, we verify that the value of postponement increases significantly for scenarios of increased uncertainty (higher volatility) and higher escape costs, as also does the optimal budget (although not proportionally to the changes in the escape costs). Additionally, the suppression/prevention budget ratio is highly sensitive to changes in escape costs, while it remains mostly insensitive to changes in volatility. Furthermore, we show the policy implications of these findings at operational (e.g., spatial solutions) and strategic levels (e.g., climate change). Exploring the impact of increasing escape costs in the optimal investment mix, we identified in our instances four qualitative system stages, which can be related to specific socioecological contexts and used as the basis for policy (re)design. In addition to questioning some popular myths, our results highlight the value of fuel treatments and the contextual nature of the optimal portfolio mix.

2018

Renewable integration through transmission network expansion planning under uncertainty

Authors
Loureiro, MV; Schell, KR; Claro, J; Fischbeck, P;

Publication
ELECTRIC POWER SYSTEMS RESEARCH

Abstract
In this paper we bring together a stochastic mixed integer programming model for transmission network expansion planning, incorporating portfolios of real options to address the evolution in time of uncertain parameters, with the adjusted generalized log-transformed model, to expand the number of correlated parameters that can be modeled. We apply these methods to evaluate the potential contribution of underwater transmission investments to increase renewables penetration in the Azores archipelago. The approach also includes expansion lead times, due to the large timespans involved in the construction of new transmission lines. Our analysis focuses on a set of the four closest islands in the archipelago, Pico, Faial, S. Jorge and Terceira, and shows that even though investments are delayed and the future network configuration varies according to the evolution of renewable generation scenarios, an investment in underwater transmission, if technically feasible, within the assumptions of our model, could in fact contribute to increase renewables penetration, by enabling islands with an excess in generation from renewable sources to supply other islands in a deficit situation.

2017

Probabilistic cost prediction for submarine power cable projects

Authors
Schell, KR; Claro, J; Guikema, SD;

Publication
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Abstract
It is estimated that Europe alone will need to add over 250,000 km of transmission capacity by 2050, if it is to meet renewable energy production goals while maintaining security of supply. Estimating the cost of new transmission infrastructure is difficult, but it is crucial to predict these costs as accurately as possible, given their importance to the energy transition. Transmission capacity expansion plans are often founded on optimistic projections of expansion costs. We present probabilistic predictive models of the cost of submarine power cables, which can be used by policymakers, industry, and academia to better approximate the true cost of transmission expansion plans. The models are both generalizable and well specified for a variety of submarine applications, across a variety of regions. The best performing statistical learning model has slightly more predictive power than a simpler, linear econometric model. The specific decision context will determine whether the extra data gathering effort for the statistical learning model is worth the additional precision. A case study illustrates that incorporating the uncertainty associated with the cost prediction to calculate risk metrics - value-at-risk and conditional-value-at-risk provides useful information to the decision-maker about cost variability and extremes.

Supervised
thesis

2018

Demand forecasting in a multi-specialty hospital setting: a comparative study of machine learning and classical statistical methods

Author
Carlos Miguel Ferreira Alves

Institution
UP-FEUP

2018

Learning from HTTP/2 encrypted traffic: a machine learning-based analysis tool

Author
Diogo Belarmino Coelho Marques

Institution
UP-FEUP

2018

Exploring search engine counts in the identification and characterization of search queries

Author
Diogo Magalhães Moura

Institution
UP-FEUP

2018

Enhancing Game-based Software Project Estimation Learning with Personality Traits

Author
Diogo Miguel Sousa Barroso

Institution
UP-FEUP

2018

Towards a Live Software Development Environment

Author
Diogo da Silva Amaral

Institution
UP-FEUP