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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

Baterias 2030: renewable production technologies at the service of the community

Photovoltaic solar modules for facades, wind systems and pavements capable of generating power: welcome to the cities of the future! The journey that began in 2020, towards promoting the decarbonisation of cities through renewable energy communities, is now coming to an end.

17th August 2023

“Empresas e Academia – O novo compromisso para a inovação” – INESC TEC participated in an event promoted by Agência Lusa and COTEC Portugal

What is the relationship between companies and universities? What path should Portugal follow to leverage innovation partnerships that boost industrialisation and growth? These were some of topics discussed during an event promoted by Agência Lusa and COTEC Portugal, featuring INESC TEC.

28th July 2023

Ecovale: energy efficiency training sessions kicked off in Barcelos and Vila Nova de Famalicão

Is the refrigerator the top energy-consuming appliance in my home? Is my house ready for the temperature changes during winter and summer? What energy efficiency actions can I adopt?  How can I monitor my household energy consumption? These and other issues were addressed in the first workshops of the Ecovale project, aimed at the population of Barcelos and Vila Nova de Famalicão; the events brought together dozens of citizens from both cities.

28th July 2023

Power and Energy

Smart Grids: INESC TEC promotes training action in liaison with the Atlantic Technical University

During the months of June and July, INESC TEC will carry out a capacity-building action dedicated to "Smart Grids" targeting the professors at the Higher Institute of Engineering and Marine Sciences (ISECMAR) of the Atlantic Technical University (UTA), Cape Verde, as well as a training initiative with business actors part of the electricity sector. This action follows the cooperation agreement between INESC TEC and UTA, signed in 2022.

21st June 2023

Industrial and Systems Engineering

A more sustainable winemaking sector: INESC TEC supports the implementation of robotic technologies to reduce the ecological footprint

The main objective of the Vine & Wine Portugal Agenda, supported by the Recovery and Resilience Plan (PRR) is to promote energy transition and digital innovation, while generating added value through the Portuguese winemaking sector. The development or presentation of mature technologies will be a key-element of the project, enabling the promotion of a more sustainable and environmentally friendly production – aspects that are quite relevant to a sector that’s highly influenced by climate change.

15th May 2023

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Featured Projects

RAM_PLAN_GREEN_PORT

Estudos no âmbito do sistema elétrico das ilhas da Madeira e do Porto Santo

2023-2024

Team
001

Laboratories

Laboratory of Smart Grids and Electric Vehicles

Publications

CPES Publications

View all Publications

2024

A Novel Three-Phase Multiobjective Unified Power Quality Conditioner

Authors
Monteiro, V; Moreira, C; Lopes, JAP; Antunes, CH; Osorio, GJ; Catalao, JPS; Afonso, JL;

Publication
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Abstract
The decarbonization of the economy and the increasing integration of renewable energy sources into the generation mix are bringing new challenges, requiring novel technological solutions in the topic of smart grids, which include smart transformers and energy storage systems. Additionally, power quality is a vital concern for the future smart grids; therefore, the continuous development of power electronics solutions to overcome power quality problems is of the utmost importance. In this context, this article proposes a novel three-phase multiobjective unified power quality conditioner (MO-UPQC), considering interfaces for solar PV panels and for energy storage in batteries. The MO-UPQC is capable of compensating power quality problems in the voltages (at the load side) and in the currents (at the power grid side), while it enables injecting power into the grid (from the PV panels or batteries) or charging the batteries (from the PV panels or from the grid). Experimental results were obtained with a three-phase four-wire laboratory prototype, demonstrating the feasibility and the large range of applications of the proposed MO-UPQC.

2023

Estimation of Planning Investments with Scarce Data - comparing LASSO, Bayesian and CMLR

Authors
Fidalgo, JN; Macedo, PM; Rocha, HFR;

Publication
2023 19TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.

2023

Easing Predictors Selection in Electricity Price Forecasting with Deep Learning Techniques

Authors
Silva, AR; Fidalgo, JN; Andrade, JR;

Publication
2023 19TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
This paper explores the application of Deep Learning techniques to forecast electricity market prices. Three Deep Learning (DL) techniques are tested: Dense Neural Networks (DNN), Long Short-Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN); and two non-DL techniques: Multiple Linear Regression and Gradient Boosting (GB). First, this work compares the forecast skill of all techniques for electricity price forecasting. The results analysis showed that CNN consistently remained among the best performers when predicting the most unusual periods such as the Covid19 pandemic one. The second study evaluates the potential application of CNN for automatic feature extraction over a dataset composed by multiple explanatory variables of different types, overcoming part of the feature selection challenges. The results showed that CNNs can be used to reduce the need for a variable selection phase.

2023

A Data-Driven Approach to Estimate the Flexibility Maps in Multiple TSO-DSO Connections

Authors
Silva, J; Sumaili, J; Silva, B; Carvalho, L; Retorta, F; Staudt, M; Miranda, V;

Publication
IEEE TRANSACTIONS ON POWER SYSTEMS

Abstract
This paper presents a methodology to estimate flexibility existing on TSO-DSO borderline, for the cases where multiple TSO-DSO connections exist (meshed grids). To do so, the work conducted exploits previous developments regarding flexibility representation through the adoption of active and reactive power flexibility maps and extends the concept for the cases where multiple TSO-DSO connection exists, using data-driven approach to determine the equivalent impedance between TSO nodes, preserving the anonymity regarding sensitive grid information, such as the topology. This paper also provides numerical validation followed by real-world demonstration of the methodology proposed.

2023

Economic Analysis of a Hydrogen Power Plant in the Portuguese Electricity Market

Authors
Rodrigues, LM; Soares, T; Rezende, I; Fontoura, JP; Miranda, V;

Publication
ENERGIES

Abstract
Hydrogen is regarded as a flexible energy carrier with multiple applications across several sectors. For instance, it can be used in industrial processes, transports, heating, and electrical power generation. Green hydrogen, produced from renewable sources, can have a crucial role in the pathway towards global decarbonization. However, the success of green hydrogen production ultimately depends on its economic sustainability. In this context, this work evaluates the economic performance of a hydrogen power plant participating in the electricity market and supplying multiple hydrogen consumers. The analysis includes technical and economical details of the main components of the hydrogen power plant. Its operation is simulated using six different scenarios, which admit the production of either grey or green hydrogen. The scenarios used for the analysis include data from the Iberian electricity market for the Portuguese hub. An important conclusion is that the combination of multiple services in a hydrogen power plant has a positive effect on its economic performance. However, as of today, consumers who would wish to acquire green hydrogen would have to be willing to pay higher prices to compensate for the shorter periods of operation of hydrogen power plants and for their intrinsic losses. Nonetheless, an increase in green hydrogen demand based on a greater environmental awareness can lead to the need to not only build more of these facilities, but also to integrate more services into them. This could promote the investment in hydrogen-related technologies and result in changes in capital and operating costs of key components of these plants, which are necessary to bring down production costs.

Facts & Figures

12R&D Employees

2020

9Academic Staff

2020

24Senior Researchers

2016

Contacts