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    Power and Energy Systems

    The Centre for Power and Energy Systems (CPES) is a global reference in the large-scale integration of renewable-based electricity generation into the power system.


    We research and develop advanced solutions that enable the monitoring, control, optimisation, and forecasting of electricity production and consumption, as well as multi-energy systems. Our work supports the decarbonisation of the energy system, the electrification of society, and the large-scale integration of renewables, making power grids more flexible, intelligent, and resilient.


    Our research results include models, methodologies, hardware, and software (much of it open source) designed for various energy sector stakeholders, including citizens, communities, service providers, system operators, regulators, policy-makers, and governmental bodies.


    We operate a Smart Grids and Electric Vehicles Laboratory – the X-Energy Lab (link) – where we validate technological advancements in real-world conditions, ensuring that our solutions have a tangible impact on the energy sector.


    Thanks to our expertise, we hold a prominent role in both national and international projects supporting the energy transition, while also providing direct services to key players in the electricity sector.

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    Publications

    CPES Publications

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    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

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    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.

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    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.

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    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.

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    2026

    Flexibility optimization from distributed storage resources under stochastic uncertainties

    Authors
    Pinheiro, LV; De Barros, TR; De Oliveira, LW; Oliveira, JG; Soares, TA; Dias, BH;

    Publication
    ELECTRIC POWER SYSTEMS RESEARCH

    Abstract
    The present work proposes a two-stage optimization approach for flexibility services provided by battery energy storage systems (BESS) in distribution networks with photovoltaic (PV) generation and electric vehicles (EV). The considered flexibility services include reserve allocation and voltage regulation to support network operation. The first stage optimizes the day-ahead (DA) scheduling of distributed BESS to minimize overall costs, including energy, BESS usage, and reserve, while accounting for stochastic variations in load, PV generation, and EV penetration. The second stage simulates the real-time (RT) operation of the electrical distribution network, evaluating system behavior under different scenarios based on DA decisions. A coordinated control strategy is applied, integrating DA scheduling with network voltage levels. Deviations between BESS outputs in DA and RT stages are fed back into a new DA run to adjust outputs and reduce costs. Results on a medium-voltage distribution system with 157 nodes (based on a reduced version of the EPRI CKT5 feeder) demonstrate that the proposed scenario-based model provides feasible solutions under uncertainty, with BESS playing a key role while strictly adhering to planned operational modes from DA to RT, as typically enforced in energy market participation.

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