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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 can we accelerate the energy transition towards the market? The second edition of The Shape of Energy to Come sought to provide answers

The energy sector is undergoing the greatest transformation of recent decades, and INESC TEC seeks to lead the discussion. The second edition of The Shape of Energy to Come brought together around 300 researchers, companies and decision-makers to debate the issues that will define the future: smarter and more resilient grids, the e-mobility revolution, industrial decarbonisation, the growing role of Artificial Intelligence, and the need for partnerships capable of accelerating real innovation.

17th December 2025

Power and Energy Systems

INESC TEC researcher wins Vestas Award with renewable energy forecasting solution

How can we improve renewable energy forecasts without undermining competitiveness between companies? Luís Rodrigues, researcher at INESC TEC, seems to have an answer - and his idea has been recognised with the Vestas Award for best master’s thesis.

12th December 2025

Power and Energy Systems

European partners of the ENFIELD project met in Porto to discuss challenges and opportunities for AI in the energy sector

Two days, more than 50 participants, and one conviction: Artificial Intelligence (AI) is undoubtedly one of the most powerful allies in Europe’s energy transition. The second Energy & Green AI workshop - organised by INESC TEC and SINTEF Digital under the European project ENFIELD - brought together experts from several countries to discuss the challenges and opportunities at the intersection of AI and energy systems.

12th December 2025

Power and Energy Systems

INESC TEC among the authors of the European roadmap for AI foundation models in energy

The goal? To advance the development of a European foundation model of Artificial Intelligence (AI) for energy grids - a strategic project for the future of the European Union’s (EU) energy system. The means? The “Workshop on the European AI Foundation Model for Energy Grids”, organised by the European Commission, Fraunhofer FIT, INESC TEC and NTUA (namely DG ENER) on 8 December, in Brussels. The actors? First and foremost, INESC TEC, which played an active and key role at the event, with researcher Ricardo Bessa representing the Institute, but also electricity distribution and transmission system operators (DSOs and TSOs), other research institutions, European AI Factories, sector associations and representatives of the European Commission.

11th December 2025

Power and Energy Systems

A path of excellence: Ricardo Bessa elevated to IEEE Fellow

Another INESC TEC researcher has been elevated to IEEE Fellow – one of the most prestigious recognitions in the field of engineering: Ricardo Bessa, a researcher at INESC TEC - where he leads the Power & Energy Systems area -, was acknowledged for his “contributions to renewable energy forecasting and the integration in decision-support tools,” as stated in the note sent by Kathleen A. Kramer, IEEE President and CEO.

04th December 2025

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001

Laboratories

Laboratory of Smart Grids and Electric Vehicles

Publications

CPES Publications

View all Publications

2026

Industrial Application of High-Temperature Heat and Electricity Storage for Process Efficiency and Power-to-Heat-to-Power Grid Integration

Authors
Coelho A.; Silva R.; Soares F.J.; Gouveia C.; Mendes A.; Silva J.V.; Freitas J.P.;

Publication
Lecture Notes in Energy

Abstract
This chapter explores the potential of thermal energy storage (TES) systems towards the decarbonization of industry and energy networks, considering its coordinated management with electrochemical energy storage and renewable energy sources (RES). It covers various TES technologies, including sensible heat storage (SHS), latent heat storage (LHS), and thermochemical energy storage (TCS), each offering unique benefits and facing specific challenges. The integration of TES into industrial parks is highlighted, showing how these systems can optimize energy manage-ment and reduce reliance on external sources. A district heating use case also demonstrates the economic and environmental advantages of a multi-energy management strategy over single-energy approaches. Overall, TES technologies are presented as a promising pathway to greater energy effi-ciency and sustainability in industrial processes.

2026

A federated Artificial Intelligence testing and experimentation facility for the European energy sector

Authors
Sarmas, E; Lucas, A; Acosta, AF; Ponci, F; Rodriguez, P; Marinakis, V;

Publication
Engineering Applications of Artificial Intelligence

Abstract
The application of Artificial Intelligence (AI) in the energy sector offers new opportunities for developing flexible, efficient, and sustainable infrastructures. Nevertheless, real-world deployment is still constrained by the lack of large-scale, integrated environments that can evaluate advanced algorithms under realistic operating conditions while ensuring regulatory compliance. This paper presents EnerTEF (which stands for Energy Testing and Experimentation Facility), a federated platform for testing and experimentation in the energy sector designed to address this gap. We introduce a unified TEF architecture that enables full-stack evaluation of intelligent systems, including predictive modeling, optimization, learning under data distribution shifts and federated learning across geographically distributed sites. The framework integrates high-fidelity digital twins, a privacy-preserving data exchange framework and regulatory sandboxing to support transparent, explainable and robust AI development. EnerTEF demonstrates how such a framework can be deployed in critical energy domains through three real-world scenarios including short-term hydropower generation forecasting, coordination between distribution network operators and distributed energy resources and real-time optimization of self-consumption for municipal buildings. Results show that EnerTEF effectively enables the development of novel AI models, improves cross-context generalizability and supports innovation for complex energy infrastructures, ultimately creating a practical, scalable path for addressing different energy-related problems and heterogeneous data. © 2025 The Authors.

2025

Probabilistic Estimation of the Quality-of-Service Indexes in Distribution Networks

Authors
Branco, JPTS; Macedo, P; Fidalgo, JN;

Publication
2025 21ST INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
Ensuring reliable and high-quality electricity service is critical for consumers and Distribution System Operators (DSO). The DSO's Plan for Development and Investment in the Distribution Network (PDIDN) plays a pivotal role in enhancing network reliability and resilience while balancing technical and financial aspects. This study proposes a novel probabilistic approach for quality-of-service (QoS) estimation in distribution systems, addressing the limitations of traditional deterministic methods. Leveraging Bayesian regression, specifically the Spike and Slab technique, the model incorporates prior knowledge to improve the prediction of key QoS indicators such as SAIDI, SAIFI, and TIEPI. Using historical network data, the model demonstrates superior predictive accuracy and robustness, offering realistic confidence intervals for strategic planning. This method enables informed investments, enhances regulatory compliance, and supports renewable integration. The findings underline the potential of probabilistic modeling in advancing QoS forecasting, encouraging its application in other areas of electric network management.

2025

Analysis and Optimization of Battery Energy Storage Systems in Energy Markets

Authors
Baptista, G; Fidalgo, JN;

Publication
2025 21ST INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
This article explores the optimization of Battery Energy Storage Systems (BESS) in energy markets, emphasizing their role in decarbonization by storing excess renewable energy and mitigating grid constraints. BESS enables energy transition by facilitating energy arbitrage, frequency regulation, and grid stabilization, essential for integrating variable renewable sources. Focusing on the UK energy market, the study highlights the favorable policies and investments driving BESS deployment. It examines revenue streams, including Day-Ahead and Intraday markets, ancillary services, and balancing mechanisms, particularly dynamic services like frequency regulation. Challenges such as gas market volatility and regulatory hurdles are also discussed. The proposed market optimization model simulates BESS operations, revealing consistent revenue potential influenced by market conditions and regulatory frameworks. The study underscores BESSs critical role in stabilizing grids, supporting renewables, and enhancing energy security while calling for further research on equipment degradation and broader impacts on energy systems and pricing.

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.

Facts & Figures

24Senior Researchers

2016

12R&D Employees

2020

78Researchers

2016

Contacts