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A Simulation of Market-based Non-frequency Ancillary Service Procurement Based on Demand Flexibility

Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 781-792页

作者： Ricardo Faia Fernando Lezama Tiago Pinto Pedro Faria Zita Vale José Manuel Terras Susete Albuquerque Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development(GECAD) R.Dr.António Bernardino de Almeida4314249-015PortoPortugal E-REDES Distribuição de Eletricidade S.A.R.Camilo Castelo Branco43LisboaPortugal

This paper proposes a novel approach for the provision of non-frequency ancillary service(AS)by consumers connected to low-voltage distribution *** proposed approach considers an asymmetric pool-based local market for AS negotiation,allowing consumers to set a flexibility quantity and desired price to trade.A case study with 98 consumers illustrates the proposed market-based non-frequency AS provision ***,three different strategies of consumers’participation are implemented and tested in a real low-voltage distribution network with radial *** is shown that consumers can make a profit from the sale of their flexibility while contributing to keeping the network power losses,voltage,and current within pre-defined ***,the results demonstrate the value of AS coming directly from end-users.

关键词： Demand flexibility local electricity market ancillary service low-volatge distribution network

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Benchmarking Large Language Models for Multi-agent Systems: A Comparative Analysis of AutoGen, CrewAI, and TaskWeaver 22nd

Benchmarking Large Language Models for Multi-agent Systems: ...

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22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Barbarroxa, Rafael Gomes, Luis Vale, Zita GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031704147

This paper presents the benchmarking of three multi-agent systems powered by large language models. The paper presents a comparative analysis of AutoGen, CrewAI, and TaskWeaver. Nowadays, large language models have emerged as powerful tools able to assist users in various areas. The integration of large language models into multi-agent systems increases their potential for collaborative problem-solving. This study focuses on a case study involving a machine learning code generation task which is used to evaluate the framework’s performance. To assess the performance of the solutions, it is requested to create energy forecasting models using the same dataset as the base. After producing the code, a new dataset is used to test the model performance using the root mean square error. The three solutions were able to provide results using multiple large language models. The best result was achieved by TaskWeaver using GPT-3.5, with an error of 25.04. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi agent systems

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Simulation and Operation Analysis of a Smart Grid Using Simulink 14th

Simulation and Operation Analysis of a Smart Grid Using Simu...

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14th International conference of the IMACS TC1 Committee, ELECTRIMACS 2022

作者： Van Waeyenberge, Alexander Canizes, Bruno Soares, João Ramos, Sérgio Ravyts, Simon Chavez, Juliana Vale, Zita GECAD Research Center Polytechnic of Porto Porto Portugal GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI—Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal ESAT ELECTA KU Leuven Ghent Belgium

ISBN: (纸本)9783031248368

Changes will be required to handle the increased power flow in the network as the distribution infrastructure ages and the number of EVs and renewable grows. Designing and operating an intelligent network that reacts to changing power flows to ensure the optimal operation is the most economical option than fortifying the network with heavier cables. This work aims to build a model of a 13 bus medium voltage distribution network with high penetration of distributed energy resources and use it to analyze network conditions. The MATLAB Simulink software is used to model and evaluate the network. The outcomes suggest the model is promising and valid even when renewable generation is at low levels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart power grids

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A Smart Contract-Based Peer-to-Peer Energy Trading System 6th

A Smart Contract-Based Peer-to-Peer Energy Trading System

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6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

作者： Abbasi, Mahmoud Faria, Pedro Gomes, Luis Prieto, Javier Vale, Zita BISITE Research Group University of Salamanca Salamanca37007 Spain GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031819278

The transition toward sustainable and decentralized energy systems has prompted the emergence of Peer-to-Peer (P2P) energy trading systems, challeng.ng traditional centralized models. This paper proposes a smart contract-based P2P energy trading system to foster a resilient and sustainable energy ecosystem. By integrating blockchain technology with P2P energy trading, we introduce trust, security, and transparency into energy transactions. Our solution employs an auction-based mechanism and blockchain oracles to address standardization challeng.s and enhance the accuracy of energy-related data. Additionally, an advanced identity management system ensures secure access to the trading system. Through smart contracts, energy producers and consumers eng.ge directly in transparent and decentralized transactions. Despite challeng.s such as regulatory uncertainties and scalability concerns, our solution offers a promising alternative for building a more inclusive and sustainable energy future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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Caravels: A Decentralized Container-Based Infrastructure for Sustainable Human-Centric intelligent Energy Communities 19th

Caravels: A Decentralized Container-Based Infrastructure for...

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19th International Conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Silva, Rafael Gomes, Luis Vale, Zita GECAD Research Group On Intelligent Engineering and Computing for Advanced Innovation and Development LASI—Intelligent Systems Associate Laboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031750120

As the European Union sets a target to increase renewable energy consumption by 2030, progressing towards a decentralized energy production becomes more a (greater?) priority. This can be achieved with initiatives such as energy communities, which when combined with active consumer participation and the integration of IoT devices and Cloud computing technologies in households can enhance energy management, however, it also introduces privacy concerns. Single-board computers are cost-effective computing solutions, but in turn face challeng.s with scalability and reliability. To address these issues, we propose Caravels, a distributed solution that leverages container applications for intelligent energy communities. Caravels promotes direct peer-to-peer communication among users, enabling real-time applications and collaborative deployments while ensuring user data privacy and system scalability. We conducted a case study that implements Caravels across Raspberry Pi nodes, demonstrating its feasibility and adaptability. The results indicate Caravels’ promising potential to empower households towards sustainable energy management and overcoming technological constraints and privacy concerns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Scalability

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A methodology for energy key performance indicators analysis

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Energy Informatics 2021年第1期4卷 1-15页

作者： Faria, Pedro Lezama, Fernando Vale, Zita Khorram, Mahsa GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal Polytechnic of Porto Porto Portugal

With the advent of the smart grid era, the electrical grid is becoming a complex network in which different technologies coexist to bring benefits to both customers and operators. This paper presents a methodology for analyzing Key Performance Indicators (KPIs), providing knowledge about the performance and efficiency of energy systems, focusing on the demand side. In the first stage of the methodology, the baseline KPIs are calculated. In the second stage, all KPIs are updated to be compared with the baseline ones. In fact, due to the dynamic nature of players in a smart grid, this methodology plays a crucial role in the performance assessment. Moreover, the proper definition and selection of KPIs is usually a challeng.ng task since KPIs can be applied to evaluate diverse areas within a smart grid. Such areas include building energy efficiency, home communications, and smart metering deployment, just to mention a few. In the proposed methodology, the information obtained from a KPI can be driven to distinct aspects such as efficiency, environment, reliability, power quality, safety, security, or cost reduction. Through a case study from a real implementation of an energy system, we show how to assess energy consumption efficiency, thus improving energy management. © 2021, The Author(s).

关键词： Smart power grids

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Analyzing the Impact of Pricing Strategies on Economic Fairness in Energy Communities

Analyzing the Impact of Pricing Strategies on Economic Fairn...

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IEEE General Meeting Power& Energy Society

作者： Calvin Goncalves Fernando Lezama Zita Vale Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) LASI - Polytechnic of Porto Porto Portugal

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

This article proposes a comprehensive framework encompassing energy sharing optimization, pricing schemes for energy transactions, and fairness metrics to validate the profit distribution of members in an energy community. First, a mixed-integer linear programming model is introduced to optimize energy sharing among the members of an energy community. Second, the study provides insights into the impact of four pricing mechanisms on economic fairness within the energy community. Finally, to validate the profit distribution, two fairness metrics assessing the quality of experience and the profit gaps are integrated to ensure economic equity within the energy community. The results shed light on the achieved level of fairness, evaluating the effectiveness of diverse pricing methods and the collaborative energy-sharing dynamics within the energy community.

关键词： Measurement Costs Scalability Buildings Collaboration Pricing Mixed integer linear programming Quality of experience Sustainable development Optimization

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Learning-Based Models for intelligent Control Over Air Conditioning Units in a Smart Building 19th

Learning-Based Models for Intelligent Control Over Air Condi...

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19th International Conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Ribeiro, Bruno Silva, Rafael Mota, Bruno Gomes, Luis Vale, Zita Polytechnic of Porto Porto Portugal GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI—Intelligent Systems Associate Laboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031750120

In power and energy systems, artificial intelligence is commonly used for energy forecasting and decision-making. However, the real deployment of artificial intelligence models for operation in smart buildings is not common, and there is a significant gap between the literature-proposed models and deployed models. In this research, the authors apply multiple machine learning models to learn user-profiles and comfort levels regarding air conditioning usage: decision tree, random forest, support vector machine, and artificial neural network. To validate the models, a real use case with a one-year dataset was used. The dataset preparation is also part of this research. The models were analyzed using simulation and the best solution, in this case support vector machine, was deployed in a real uncontrollable environment using a containerized-based solution. The promising results demonstrate that the proposed methodology can be deployed in real buildings and learn the air conditioning usage profiles within one year of use. The authors also proceed with the deployment of a learning model in a smart building. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Open-access tools for the modelling and simulation of electricity markets

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Energy Informatics 2022年第4期5卷 43页

作者： Carvalho, Rui Santos, Gabriel Vale, Zita GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Institute of Engineering Polytechnic of Porto Rua Dr. Antonio Bernardino de Almeida 431 Porto4249-015 Portugal

Globally, the amount of renewable energy generation is increasing, which raises the complexity of operating electrical grids to maintain stability and balance and boosts the need for developing new electricity market (EM) models fitting this new reality. To test, study, and validate the possible effects of novel EM designs, simulation techniques are frequently employed. This work proposes the use of two open-access tools for the modeling and simulation of complex EMs. These are the Electricity Markets Service (EMS) and the Spine Toolbox. EMS enables the simulation of two commonly used auction-based algorithms and the execution of three European wholesale EMs. Being published as a web service facilitates its integration with other services, systems, or software agents, such as the Spine Toolbox. The Spine Toolbox, in turn, is an open-source software for complex energy systems modeling. Combining them allows the modeling and simulation of complex EMs from the wholesale to local markets, as well as testing and validating new market designs. This work’s case study demonstrates how to use these tools to simulate the operation of the Iberian EM – MIBEL – for a month, using public data available from the market operator’s website. The results are analyzed from the perspective of the market operator and two players, i.e., a selected buyer and seller, for a specific day and the whole month. © 2022, The Author(s).

关键词： Web services

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Optimizing Energy Costs in Energy Communities: A Monthly Tariff Clustering Approach

Optimizing Energy Costs in Energy Communities: A Monthly Tar...

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International Conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Fernando Lezama Diego Bairrao Filipe Doria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Polytechnic of Porto Porto Portugal

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

In collaborative energy communities, optimizing energy costs is a critical aspect of sustainable management. This article explores the potential benefits of applying clustering algorithms to vary retail tariffs monthly, aiming to reduce energy bills for the community as a whole. The article compares a traditional approach of applying the same tariff to all community members throughout the year with a novel approach of dynamically changing tariffs based on monthly clustering results. A case study is conducted, wherein energy bill costs per month are analyzed under different tariff scenarios utilizing k-means clustering. Results indicate that the proposed approach yields promising reductions in energy costs, up to 8.76% (1170.18 EUR) improvement compared to the traditional method. The study contributes valuable insights into the practical application of clustering in energy community management and highlights the potential for significant cost savings through dynamic tariff adjustments.

关键词： Costs Accuracy Heuristic algorithms Switching frequency Tariffs Clustering algorithms Switches Power systems intelligent systems Optimization

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