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Facts & Numbers
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Presentation

Power and Energy Systems

The centre is a world reference in large-scale integration of Distributed Energy Resources. Our expertise led us to take on key roles in important EU projects and also led to contracts for development and consultancy with manufacturing equipment companies and with power generation, transmission and distribution companies, regulators, government agencies and investors in Europe, South America, the United States of America and Africa.

At CPES, we address the following main research areas: Decision Making, Optimisation and Computational Intelligence, Forecasting, Static and Dynamic analysis of Energy Grids, Reliability, Power Electronics.

Part of our activity is developed in the Laboratory of Smart Grids and Electric Vehicles that supports the validation of major developments in a real environment.

Over the last years, we have made several developments in the electrical network planning and operation, namely the inclusion of distributed energy resources forecasting and network  optimisation tools embedded in different voltage layers, exploiting the MicroGrid hierarchical concept. Relevant steps were given on the inclusion of computational intelligence in control algorithms that were demonstrated under real conditions in several pilots.   

Latest News

Engineers, this news is for you: a new book on programming and AI with real-world solutions

Imagine a compendium of programming concepts, numerical computation, and advanced applications that, beyond theory, demonstrates how to apply these tools to real engineering problems. That is the premise of the book MATLAB: From Fundamentals to Artificial Intelligence by researcher Filipe Azevedo.

10th September 2025

Power and Energy Systems

Between scientific papers, code or conferences: what might a day in the life of a woman in engineering look like?

Bruna Tavares, a researcher at INESC TEC, was one of the guest speakers at the session Women in Engineering: Percurso Académico e Carreira Profissional, organised by the Power and Energy Society (PES) and Vehicular Technology Society (VTS) chapters of IEEE Portugal. Alongside Beatriz Simões, an engineer at EDP Renováveis, Bruna shared her academic and research journey in the field of power & energy systems.

21st July 2025

Power and Energy Systems

Multiple nationalities enter an auditorium - what happens next? They discuss science and celebrate multiculturalism

At INESC TEC, multiculturalism is celebrated in many ways, with several initiatives led by groups like the Diversity and Inclusion Commission. But there are also groups of researchers who choose to combine scientific debate with a celebration of cultural diversity. Every month, researchers in the power & energy domain gather for what they call scientific or brainstorming sessions. And then? They celebrate multiculturalism in various ways; most recently, they did so through a multicultural afternoon snack break, where food took centre stage.

08th July 2025

Power and Energy Systems

From the lab to the power grid: INESC TEC leads Artificial Intelligence experimentation

INESC TEC is at the forefront of developing, validating, and certifying Artificial Intelligence (AI) applications in the energy sector. The AI-EFFECT project - a distributed infrastructure across Europe that virtually connects existing facilities - will include an experimental node, focusing on local energy communities and microgrids.

30th June 2025

Power and Energy Systems

INESC TEC promotes national pilot to increase consumption flexibility using Artificial Intelligence

The institution is promoting the development of the Portuguese pilot of the European project Hedge-IoT, which aims to demonstrate the potential of Artificial Intelligence (AI) in delivering flexible energy services. The pilot - Living Lab for Interoperable AI-based Energy Services - is being implemented in partnership with CEVE (Cooperativa Elétrica do Vale d’Este), featuring both residential consumers and commercial buildings.

16th June 2025

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Featured Projects

RIFF

Research Infrastructures for the Future of Ukraine: Roadmap for Sustained Growth and Recovery

2025-2028

Perfis_Perdas_2026

Determinação de Perfis de Perdas e de Fatores de Ajustamento para Perdas para 2026

2025-2025

Hibrid_Douro

Avaliação do cumprimento do código de rede do projeto de hibridização do parque eólico do Alto Douro

2025-2025

Hibrid_Raia

Avaliação do cumprimento do código de rede do projeto de hibridização do parque eólico de Raia

2025-2025

CampusREN2025

Formação Avançada para a REN CAMPUS REN2025

2025-2025

REATIVA_MINHO

Estudo de otimização de potência reativa do Parque Eólico do Alto Minho I

2024-2025

EnerTEF

Common European-scale Energy Artificial Intelligence Federated Testing and Experimentation Facility

2024-2027

Data4LEM

Synthetic and Explainable Data Generation for the Simulation and Analysis of Future Local Electricity Markets

2024-2025

MORADIST

PS-MORA Arquitetura distribuída

2021-2025

Meteo_NMP_Forecast

2015-2016

SiMicrogrids

2015-2015

Team
001

Laboratories

Laboratory of Smart Grids and Electric Vehicles

Publications

CPES Publications

View all Publications

2025

Fuzzy Logic Estimation of Coincidence Factors for EV Fleet Charging Infrastructure Planning in Residential Buildings

Authors
Carvalhosa, S; Ferreira, JR; Araújo, RE;

Publication
Energies

Abstract
As electric vehicle (EV) adoption accelerates, residential buildings—particularly multi-dwelling structures—face increasing challenges to electrical infrastructure, notably due to conservative sizing practices of electrical feeders based on maximum simultaneous demand. Current sizing methods assume all EVs charge simultaneously at maximum capacity, resulting in unnecessarily oversized and costly electrical installations. This study proposes an optimized methodology to estimate accurate coincidence factors, leveraging simulations of EV user charging behaviors in multi-dwelling residential environments. Charging scenarios considering different fleet sizes (1 to 70 EVs) were simulated under two distinct premises of charging: minimization of current allocation to achieve the desired battery state-of-charge and maximization of instantaneous power delivery. Results demonstrate significant deviations from conventional assumptions, with estimated coincidence factors decreasing non-linearly as fleet size increases. Specifically, applying the derived coincidence factors can reduce feeder section requirements by up to 86%, substantially lowering material costs. A fuzzy logic inference model is further developed to refine these estimates based on fleet characteristics and optimization preferences, providing a practical tool for infrastructure planners. The results were compared against other studies and real-life data. Finally, the proposed methodology thus contributes to more efficient, cost-effective design strategies for EV charging infrastructures in residential buildings.

2025

The Role of Flexibility Markets in Maintenance Scheduling of MV Networks

Authors
Tavares, B; Soares, F; Pereira, J; Gouveia, C;

Publication
International Conference on the European Energy Market, EEM

Abstract
Flexibility markets are emerging across Europe to improve the efficiency and reliability of distribution networks. This paper presents a methodology that integrates local flexibility markets into network maintenance scheduling, optimizing the process by contracting flexibility to avoid technical issues under the topology defined to operate the network during maintenance. A meta-heuristic approach, Evolutionary Particle Swarm Optimization (EPSO), is used to determine the optimal network topology. © 2025 IEEE.

2025

Location of grid forming converters when dealing with multi-class stability problems

Authors
Fernandes, F; Lopes, JP; Moreira, C;

Publication
IET GENERATION TRANSMISSION & DISTRIBUTION

Abstract
This work proposes an innovative methodology for the optimal placement of grid-forming converters (GFM) in converter-dominated grids while accounting for multiple stability classes. A heuristic-based methodology is proposed to solve an optimisation problem whose objective function encompasses up to 4 stability indices obtained through the simulation of a shortlist of disturbances. The proposed methodology was employed in a modified version of the 39-bus test system, using DigSILENT Power Factory as the simulation engine. First, the GFM placement problem is solved individually for the different stability classes to highlight the underlying physical phenomena that explain the optimality of the solutions and evidence the need for a multi-class approach. Second, a multi-class approach that combines the different stability indices through linear scalarisation (weights), using the normalised distance of each index to its limit as a way to define its importance, is adopted. For all the proposed fitness function formulations, the method successfully converged to a balanced solution among the various stability classes, thereby enhancing overall system stability.

2025

Multi-criteria placement and sizing of utility-scale grid forming converters

Authors
Fernandes, FS; Lopes, JP; Moreira, CL;

Publication
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Abstract
This work proposes a robust methodology for the location and sizing of grid forming (GFM) converters that simultaneously considers the solution costs and the security gains while accounting for the TSO nonlinear cost-security sensitivity. Such methodology, which includes a collection of techniques to reduce the problem dimensionality, formulates the placement problem as a non-linear multi-criteria decision support problem and uses a solution-seeking algorithm based on Bayesian Optimisation to determine the solution. To ease comprehension, a modified version of the IEEE 39 Test System is used as a case study throughout the method's detailed explanation and application example. A sensitivity analysis of the GFM converter's over-current capacity in the solution of the formulated placement problem is also performed. The results show that the proposed method is successful in finding solutions with physical meaning and that respect the decision agent preferences.

2025

Budget-Constrained Collaborative Renewable Energy Forecasting Market

Authors
Gonçalves, C; Bessa, RJ; Teixeira, T; Vinagre, J;

Publication
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY

Abstract
Accurate power forecasting from renewable energy sources (RES) is crucial for integrating additional RES capacity into the power system and realizing sustainability goals. This work emphasizes the importance of integrating decentralized spatio-temporal data into forecasting models. However, decentralized data ownership presents a critical obstacle to the success of such spatio-temporal models, and incentive mechanisms to foster data-sharing need to be considered. The main contributions are a) a comparative analysis of the forecasting models, advocating for efficient and interpretable spline LASSO regression models, and b) a bidding mechanism within the data/analytics market to ensure fair compensation for data providers and enable both buyers and sellers to express their data price requirements. Furthermore, an incentive mechanism for time series forecasting is proposed, effectively incorporating price constraints and preventing redundant feature allocation. Results show significant accuracy improvements and potential monetary gains for data sellers. For wind power data, an average root mean squared error improvement of over 10% was achieved by comparing forecasts generated by the proposal with locally generated ones.

Facts & Figures

24Senior Researchers

2016

0Proceedings in indexed conferences

2020

12R&D Employees

2020

Contacts