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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
Power and Energy Systems

How to manage an energy community with 10 tools

Managing an energy community could become even more efficient thanks to a set of digital tools that INESC TEC is developing within the European EMPEDOFLEX project. There are at least 10 interoperable applications in development, which will be validated in four real-world cases, designed to help municipalities, grid operators, and energy community managers reduce costs, integrate more renewable energy sources, and increase network resilience.

29th September 2025

Power and Energy Systems

Can science made in Portugal influence the EU’s energy future?

Recently, INESC TEC researchers collaborated with the Smart Grid Interoperability Laboratory of the Joint Research Centre (JRC) - the EC’s science and knowledge service that supports public policy development - to train the technical staff in semantic interoperability, a key technology for the digitalisation of Europe’s energy sector.

29th September 2025

Artificial Intelligence

INESC TEC joined the ADRF 2025 and is set to host ADRF 2026 in Porto

The AI, Data and Robotics Forum (ADRF) is one of Europe’s leading events dedicated to Artificial Intelligence (AI), data, and robotics. The 2025 edition took place in Stavanger, Norway, on September 23 and 24 - with a strong presence from INESC TEC. Before the event ended, an announcement was made: ADRF 2026 will take place in Porto, with INESC TEC responsible for organising the event.

29th September 2025

Power and Energy Systems

There are innovative technological solutions to be developed for local energy markets – with contributions from INESC TEC

The delay in generating synthetic data for time series – fundamental elements in energy forecasting scenarios – was one of the motivations for GENESIS, a project that aims to provide local electricity markets with contextual synthetic data and reliable artificial intelligence models.

22nd September 2025

Power and Energy Systems

INESC TEC writes the future of the energy transition in open source

The future of the energy transition depends on the development of open source. At least that is the conviction of INESC TEC, which took part in the Linux Foundation (LF) Energy Summit 2025, one of the most relevant events for the energy sector and the open-source community. Over two days, participants were able to explore digital tools that are already being applied in real-world contexts.  

19th September 2025

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Featured Projects

RIFF

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

2025-2028

BlackoutAddendum

Understanding the Iberian Blackout Exploiting Data from Electric Companies Associated to AELEC - Addendum

2025-2025

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

BolsasFCT_Gestao

Funding FCT PhD Grants - Management

2025-9999

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 Elsevier B.V., All rights reserved.

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

Evolving Symbolic Model for Dynamic Security Assessment in Power Systems

Authors
Fernandes, FS; Bessa, RJ; Lopes, JP;

Publication
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY

Abstract
In a high-risk sector, such as power system, transparency and interpretability are key principles for effectively deploying artificial intelligence (AI) in control rooms. Therefore, this paper proposes a novel methodology, the evolving symbolic model (ESM), which is dedicated to generating highly interpretable data-driven models for dynamic security assessment (DSA), namely in system security classification (SC) and the definition of preventive control actions. The ESM uses simulated annealing for a data-driven evolution of a symbolic model template, enabling different cooperative learning schemes between humans and AI. The Madeira Island power system is used to validate the application of the ESM for DSA. The results show that the ESM has a classification accuracy comparable to pruned decision trees (DTs) while boasting higher global inter-pretability. Moreover, the ESM outperforms an operator-defined expert system and an artificial neural network in defining preventive control actions.

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

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.

Facts & Figures

12R&D Employees

2020

60Papers in indexed journals

2020

78Researchers

2016

Contacts