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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 seeks to support the energy transition through Generative Artificial Intelligence

    As Europe’s energy system faces new challenges, generative artificial intelligence (GenAI) is emerging as a resource to optimise the operation and planning of power grids – namely in terms of increasing penetration of renewable sources, electrification of consumption and energy systems’ complexity. INESC TEC has played an active role in the development of advanced Artificial Intelligence (AI) solutions applied to the energy sector, supporting the Adra Association in the design of a strategic agenda to promote the development and adoption of GenAI in Europe’s main industrial sectors.

    24th April 2025

    Power and Energy Systems

    The Ecovale project and the legacy of energy efficiency in the community

    For two years, the Ecovale project promoted the active participation of the communities of Vila Nova de Famalicão and Barcelos in various awareness-raising activities on energy vulnerability and energy efficiency. Training activities for adults and children, an innovation platform, energy clubs in schools, and many hours of knowledge exchange later, the message is clear: we are on the right track to save more energy and become even more sustainable - with INESC TEC part of this mission.

    14th April 2025

    Power and Energy Systems

    INESC TEC and New Mexico State University signed a collaboration protocol in the field of Energy

    Promoting opportunities for exchange and scientific collaboration is one of the main objectives of the cooperation agreement between INESC TEC and the New Mexico State University (NMSU). This opportunity stemmed from the participation of a professor at NMSU, Olga Lavrova, in the first edition of INESC TEC International Visiting Researcher Programme (IIVRP) – an initiative that grants researchers from other countries the opportunity to carry out research activities at INESC TEC and NMSU, up to a maximum period of three months.

    17th January 2025

    INESC TEC developed a system to estimate the flexibility of energy consumption in supermarkets

    Refrigeration systems are responsible for a considerable share of energy consumption in the retail sector. INESC TEC led the European project InterConnect and developed a tool that addressed the consumption of said systems according to several scenarios, providing a potential source of flexibility for the DSO.  

    09th January 2025

    INESC TEC technology allows European consumers to manage their energy consumption

    INESC TEC developed a mechanism that uses data to share voluntary recommendations to consumers, reducing or increasing consumption according to the needs of the grid. The suggestions come through mobile applications and Portugal joined this endeavour.  

    03rd December 2024

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    Featured Projects

    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

    Team
    Manuel Matos

    Centre Coordinator

    Ricardo Jorge Bessa

    Centre Coordinator

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    001

    Laboratories

    Laboratory of Smart Grids and Electric Vehicles

    Publications

    CPES Publications

    View all Publications

    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.

    CloseRead Abstract

    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.

    CloseRead Abstract

    2025

    Carbon-aware dynamic tariff design for electric vehicle charging stations with explainable stochastic optimization

    Authors
    Silva, CAM; Bessa, RJ;

    Publication
    APPLIED ENERGY

    Abstract
    The electrification of the transport sector is a critical element in the transition to a low-emissions economy, driven by the widespread adoption of electric vehicles (EV) and the integration of renewable energy sources (RES). However, managing the increasing demand for EV charging infrastructure while meeting carbon emission reduction targets is a significant challenge for charging station operators. This work introduces a novel carbon-aware dynamic pricing framework for EV charging, formulated as a chance-constrained optimization problem to consider forecast uncertainties in RES generation, load, and grid carbon intensity. The model generates day-ahead dynamic tariffs for EV drivers with a certain elastic behavior while optimizing costs and complying with a carbon emissions budget. Different types of budgets for Scope 2 emissions (indirect emissions of purchased electricity consumed by a company) are conceptualized and demonstrate the advantages of a stochastic approach over deterministic models in managing emissions under forecast uncertainty, improving the reduction rate of emissions per feasible day of optimization by 24 %. Additionally, a surrogate machine learning model is proposed to approximate the outcomes of stochastic optimization, enabling the application of state-of-the-art explainability techniques to enhance understanding and communication of dynamic pricing decisions under forecast uncertainty. It was found that lower tariffs are explained, for instance, by periods of higher renewable energy availability and lower market prices and that the most important feature was the hour of the day.

    CloseRead Abstract

    2025

    Multiobjective energy management of multi-source offshore parks assisted with hybrid battery and hydrogen/fuel-cell energy storage systems

    Authors
    Kazemi-Robati, E; Varotto, S; Silva, B; Temiz, I;

    Publication
    APPLIED ENERGY

    Abstract
    With the recent advancements in the development of hybrid offshore parks and the expected large-scale implementation of them in the near future, it becomes paramount to investigate proper energy management strategies to improve the integrability of these parks into the power systems. This paper addresses a multiobjective energy management approach using a hybrid energy storage system comprising batteries and hydrogen/fuel-cell systems applied to multi-source wind-wave and wind-solar offshore parks to maximize the delivered energy while minimizing the variations of the power output. To find the solution of the optimization problem defined for energy management, a strategy is proposed based on the examination of a set of weighting factors to form the Pareto front while the problem associated with each of them is assessed in a mixed-integer linear programming framework. Subsequently, fuzzy decision making is applied to select the final solution among the ones existing in the Pareto front. The studies are implemented in different locations considering scenarios for electrical system limitation and the place of the storage units. According to the results, applying the proposed multiobjective framework successfully addresses the enhancement of energy delivery and the decrease in power output fluctuations in the hybrid offshore parks across all scenarios of electrical system limitation and combinational storage locations. Based on the results, in addition to the increase in delivered energy, a decrease in power variations by around 40 % up to over 80 % is observed in the studied cases.

    CloseRead Abstract

    2025

    Improving community-based electricity markets regulation: A holistic multi-objective optimization framework

    Authors
    Costa, VBF; Soares, T; Bitencourt, L; Dias, BH; Deccache, E; Silva, BMA; Bonatto, B; Filho, WF; Faria, AS;

    Publication
    Renewable and Sustainable Energy Reviews

    Abstract
    Community-based electricity markets, which are defined as groups of members that share common interests in renewable distributed generation, allow prosumers to embrace more active roles by opening up several opportunities for trading electricity. At the same time, such markets may favor conventional consumers by allowing them to choose cheaper electricity providers. Due to trends in power sector modernization, community-based electricity markets are of great research interest, and there are already some associated models. However, there is a research gap in searching for integrated and holistic approaches that go beyond economic aspects, consider social and environmental aspects, and assume the balanced co-existence of community-based and conventional markets. This work fills this critical research gap by adapting/applying the optimized tariff model, Bass diffusion model, life cycle assessment, and multi-objective optimization to the context of community-based markets. Results indicate that favoring conventional markets in the short term and community-based markets in the medium term is beneficial. Moreover, regulated tariffs should increase slightly in the short/medium-term to accommodate DG growth. Additionally, community-based markets can decrease electricity expenses by around 13.6 % considering the market participants. Thus, such markets can be significantly beneficial in mitigating energy poverty. © 2025 Elsevier Ltd

    CloseRead Abstract

    Facts & Figures

    60Papers in indexed journals

    2020

    78Researchers

    2016

    1Book Chapters

    2020

    View all Facts & Figures
    Contacts
    Manuel Matos

    Responsible

    +351222094230

    manuel.matos@inesctec.pt

    Ricardo Jorge Bessa

    Responsible

    +351222094230

    ricardo.j.bessa@inesctec.pt