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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

INESC TEC projects addressing energy poverty acknowledged at the LIGAR award

Two projects, one shared goal: using technology and innovation to prevent energy poverty. The Ecovale and Asprela+Sustentável projects – with INESC TEC as a partner - entered the first edition of the LIGAR award, promoted by the National Observatory on Energy Poverty (ONPE), and ranked among the top 10 submissions.

16th January 2026

Power and Energy Systems

INESC TEC among the coordinators of a new European Commission policy paper on AI and energy systems

Unlocking the potential of AI and Generative AI in European Smart Grids – a strategic position paper and guide for action is the title of the new European Commission policy paper on Artificial Intelligence (AI) and energy systems, with INESC TEC as a coordinating entity – represented by researcher Ricardo Bessa. The document is available on the European Union’s website.

15th January 2026

Focusing on ocean research and innovation, INESC TEC took centre stage at Business2Sea

At the Port of Leixões Cruise Terminal, INESC TEC placed ocean research firmly on the Business2Sea agenda; throughout the event, researchers and representatives from the Institute participated in panels, round tables and presentations, sharing experiences and future perspectives on ocean innovation.  

14th January 2026

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

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Featured Projects

EMPEDOFLEX

EMpowering Positive Energy District Optimal FLEXibility provision

2025-2028

VALAGUA

Formulação de um Modelo Computacional para a Valorização da Água no Mercado Ibérico de Eletricidade

2025-2026

Team
001

Laboratories

Laboratory of Smart Grids and Electric Vehicles

Publications

CPES Publications

View all Publications

2026

Evolving power system operator rules for real-time congestion management

Authors
Moaidi, F; Bessa, J;

Publication
Energy and AI

Abstract
The growing integration of renewable energy sources and the widespread electrification of the energy demand have significantly reduced the capacity margin of the electrical grid. This demands a more flexible approach to grid operation, for instance, combining real-time topology optimization and redispatching. Traditional expert-driven decision-making rules may become insufficient to manage the increasing complexity of real-time grid operations and derive remedial actions under the N-1 contingency. This work proposes a novel hybrid AI framework for power grid topology control that integrates genetic network programming (GNP), reinforcement learning, and decision trees. A new variant of GNP is introduced that is capable of evolving the decision-making rules by learning from data in a reinforcement learning framework. The graph-based evolutionary structure of GNP and decision trees enables transparent, traceable reasoning. The proposed method outperforms both a baseline expert system and a state-of-the-art deep reinforcement learning agent on the IEEE 118-bus system, achieving up to an 28% improvement in a key performance metric used in the Learning to Run a Power Network (L2RPN) competition. © 2025

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

Advanced Switched Reluctance Motor Control Methodologies for Electric Drive Applications

Authors
Touati, Z; Araújo, RE; Khedher, A;

Publication
Studies in Systems, Decision and Control

Abstract
Switched Reluctance Motors (SRMs) are becoming increasingly popular for various applications, including automotive applications. However, challenges such as torque ripple and vibration persist, limiting their performance. This chapter investigates the application of intelligent control strategies, particularly fuzzy logic, to mitigate these issues. Fuzzy logic modeling does not require an accurate mathematical model which is very difficult to obtain from a SRM because of its inherit nonlinearities. In this work a Fuzzy Logic Controller (FLC) applied to the speed control of an SRM, highlighting the advantages of FL over traditional methods in terms of flexibility and performance. A comparison is made between the FLC, a Sliding Mode Control (SMC), and a Proportional Integral (PI) controller. Simulation results using MATLAB/Simulink show that the FLC substantially reduces torque ripple, offering better overall performance in terms of smoothness and robustness under varying operational conditions. The findings demonstrate that FLC offers a more effective solution than conventional approaches for SRM applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

2026

Optimized Switched Reluctance Generator Operation in Wind Energy Applications

Authors
Touati, Z; Araújo, RE; Khedher, A;

Publication
Studies in Systems, Decision and Control

Abstract
Switched reluctance generators (SRG) are one of the machines with huge potential in wind power generation due to their reliability and robust design. However, the inherent characteristics of SRGs lead to significant challenges in achieving high efficiency and low output current and torque ripple simultaneously. The performance of SRGs is hindered by conflicting requirements. To address these issues, this chapter presents an optimization control strategy aimed at improving the static performance of SRGs. The chapter discusses the application of the Particle Swarm Optimization (PSO) technique to optimize the commutation angles, specifically the turn-on (?on) and turn-off (?off) angles, for an 8/6 SRG. The proposed strategy consists of two main steps. First, a Maximum Power Point Tracking (MPPT) algorithm is implemented to maximize power output at varying rotor speeds, combined with a direct power control method to regulate the power generated by the SRG. Second, a multi-objective function is developed to evaluate the SRG performance, considering key factors such as power output, output current ripple, and torque ripple. The simulation results indicate that implementing optimized turn-on and turn-off angles leads to a reduction in torque ripple from -1.78 Nm using the conventional technique to -0.66 Nm with the proposed method, corresponding to an impressive 63% decrease. Furthermore, the optimization strategy effectively maximizes the efficiency of the system employing an MPPT approach, ensuring optimal energy conversion under varying operating conditions. Future research directions include experimental validation of the proposed control system on real hardware to assess its practical feasibility and performance under real-world operating conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

2026

Optimizing Quay Crane Operations Considering Energy Consumption

Authors
de Almeida, JPR; Carrillo Galvez, A; Moran, JP; Soares, TA; Mourão, ZS;

Publication
Lecture Notes in Computer Science

Abstract
Seaport cranes operate continuously and consume large amounts of energy while aiming to minimise containerships’ berthing time. Although previous studies have contributed to addressing the crane scheduling problem, most have focused exclusively on loading time, often overlooking the aspect of energy consumption. Furthermore, crane activity is typically modelled in a simplified manner—commonly assuming a fixed cycle duration or constant energy usage when handling a container—without accounting for the impact of variable container masses. In this study, an energy-aware quay crane scheduling formulation for container terminals is proposed, highlighting the importance of integrating an energy model into the scheduling problem. The optimisation problem is formulated as a Mixed Integer Linear Programming (MILP) model. The objective is to minimise total energy costs by reordering the sequence in which containers are handled, while respecting precedence constraints defined by the ship’s stowage plan. Two solution methods—a MILP approach solved using CPLEX and a genetic algorithm (GA)—are compared. The results indicate that, for larger containerships, the genetic algorithm provides a more efficient solution method. Moreover, incorporating detailed energy consumption models for electric cranes may significantly reduce energy costs during containership handling operations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Facts & Figures

1Book Chapters

2020

9Academic Staff

2020

78Researchers

2016

Contacts