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共 134 条记录，以下是71-80 订阅

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Optimization of electricity markets participation with QPSO

Optimization of electricity markets participation with QPSO

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International Conference on European Electricity Market, EEM

作者： Ricardo Faia Tiago Pinto Zita Vale GECAD-Research Group on Intelligent Engineering Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/IPP) Porto Portugal

All around the world, the electric sector has suffered significant changes. With these alterations, electrical systems have become international, with several countries connected by a system where the management is done in common grounds. With the incorporation of large scale distributed generation, competitiveness in electrical markets has increased as small generators unite in order to be able to compete with large producers. In this game where the main objective is to win, and the premium is money it is necessary to be keen to be able to sell the available electricity at the best possible prices. With the objective of supporting players' decisions, decision support tools play a crucial role. These tools enable market players with suggestions of actions to increase their advantage from market participation. This paper presents a Quantum-based Particle Swarm Optimization (QPSO) methodology to solve the problem of optimal participation in multiple electricity markets.

关键词： Optimization Electricity supply industry Portfolios Particle swarm optimization Biological system modeling Quantum mechanics Contracts

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Incentive-based and Price-based Demand Response to Prevent Congestion in Energy Communities

Incentive-based and Price-based Demand Response to Prevent C...

引用

International Conference on Environment and Electrical eng.neering (EEEIC)

作者： Helder Pereira Ricardo Faia Luis Gomes Pedro Faria Zita Vale Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Polytechnic of Porto (ISEP/IPP) Porto Portugal

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

Under the current strategy of the European Union, energy communities have the utmost importance for increasing active participation in local energy markets. Thus, it is essential to maintain the networks that support them operational to all the processes involving energy transactions. To solve this problem, this paper proposes two preventive demand response models, one incentive-based, and the other price-based. These models make use of a power flow algorithm, to detect the periods susceptible to grid issues, and define the needed consumption reduction in the community. The ability to apply the two demand response methods sequentially brings greater robustness to the system when it is necessary to solve critical situations that required high values of flexibility. This is remonstrated in the case study, where there is a scenario where a single program cannot not solve the issue, and an increase of 4.05 % of the price was needed even after the incentive-based demand response program.

关键词： Scalability Europe Predictive models Demand response Robustness Data models Load flow

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Anomaly Detection in Cyber-Physical Systems: Reconstruction of a Prediction Error Feature Space

Anomaly Detection in Cyber-Physical Systems: Reconstruction ...

引用

Security of Information and Networks (SIN), International Conference on

作者： Nuno Oliveira Norberto Sousa Jorge Oliveira Isabel Praça Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Porto School of Engineering (ISEP) Porto Portugal

ISBN: (纸本)9781728192673

Cyber-physical systems are infrastructures that use digital information such as network communications and sensor readings to control entities in the physical world. Many cyber-physical systems in airports, hospitals and nuclear power plants are regarded as critical infrastructures since a disruption of its normal functionality can result in negative consequences for the society. In the last few years, some security solutions for cyber-physical systems based on artificial intelligence have been proposed. Nevertheless, knowledge domain is required to properly setup and train artificial intelligence algorithms. Our work proposes a novel anomaly detection framework based on error space reconstruction, where genetic algorithms are used to perform hyperparameter optimization of machine learning methods. The proposed method achieved an F1-score of 87.89% in the SWaT dataset.

关键词： Support vector machines Machine learning algorithms Machine learning Cyber-physical systems Predictive models Prediction algorithms Security

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A Novel Approach to Network Traffic Analysis: the HERA tool

A Novel Approach to Network Traffic Analysis: the HERA tool

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Daniela Pinto Ivone Amorim Eva Maia Isabel Praça Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Porto School of Engineering Polytechnic of Porto (ISEP-IPP) Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) PORTIC – Porto Research Technology & Innovation Center Polytechnic of Porto (IPP) Porto Portugal

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Cybersecurity threats highlight the need for robust network intrusion detection systems to identify malicious behaviour. These systems rely heavily on large datasets to train machine learning models capable of detecting patterns and predicting threats. In the past two decades, researchers have produced a multitude of datasets, however, some widely utilised recent datasets generated with CICFlowMeter contain inaccuracies. These result in flow generation and feature extraction inconsistencies, leading to skewed results and reduced system effectiveness. Other tools in this context lack ease of use, customizable feature sets, and flow labelling options. In this work, we introduce HERA, a new open-source tool that generates flow files and labelled or unlabelled datasets with user-defined features. Validated and tested with the UNSW-NB15 dataset, HERA demonstrated accurate flow and label generation.

关键词： Data privacy Accuracy Network intrusion detection Telecommunication traffic Machine learning Predictive models Feature extraction Labeling Computer security

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Allocation of fixed costs considering Distributed Generation and distinct approaches of Demand Response remuneration in distribution networks

Allocation of fixed costs considering Distributed Generation...

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Clemson University Power Systems Conference (PSC)

作者： Fábio Pereira João Soares Pedro Faria Zita Vale GECAD — Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP-Polytechnic Institute of Porto Porto Portugal

In the present context the distribution networks are characterized by the increasing penetration of Distributed Generation (DG) resources, which imposes important changes in operation practice. Moreover, the concept of Demand Response (DR) is currently a very important resource. The present paper proposes a methodology that makes possible the allocation of fixed costs in distribution networks considering the integration of DG and DR resources. It considers several possibilities for consumers DR remuneration. The fixed cost allocation is determined in three phases - DC Optimal Power Flow (DC-OPF), Kirschen's tracing method, and MW-mile method. The presented case study contemplates a distribution network with 49 buses, 47 DG units, 4 external suppliers and 50 consumers.

关键词： Remuneration Resource management Density estimation robust algorithm Load flow Indexes Distributed power generation Optimal scheduling

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Risk assessment model based on centrifugal governors and artificial neural networks

Risk assessment model based on centrifugal governors and art...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Francisco Oliveira Fernando Lezama Luis Gomes João Soares Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Portugal

In today’s industry, old machines, that were not manufactured according to Industry 4.0 standards, may not be equipped with sophisticated sensors for monitoring critical values and ensuring the machine's proper health and operation. As a result, third-party sensors, such as thermometers and vibration sensors, are often integrated into these machines. Unfortunately, despite being able to obtain effective measurements, such sensors lack relativization of these values to the contextual values of each machine. This paper proposes a risk assessment model that digitally mimics a real-life centrifugal governor's operation. The system combines machine learning and data analysis and uses a context-aware algorithm that can work with single or multiple sensors to output aggregated information on a machine’s health.

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Shared Intelligence Platform for Collaborative Simulations using Sequences of Algorithms: An Electricity Market participation case study

Shared Intelligence Platform for Collaborative Simulations u...

引用

IEEE Manchester PowerTech Conference

作者： Eugenia Vinagre Tiago Pinto Isabel Praca Luis Gomes Joao Soares Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering - Polytechnic of Porto (ISEP/IPP)

ISBN: (纸本)9781509042388

SEAS Shared Intelligence (SEAS SI) is a platform for algorithms sharing and execution developed under the scope of Smart Energy Aware Systems (SEAS) project to promote the intelligent management of smart grids and microgrids, by means of the shared usage of algorithms and tools, while ensuring code and intellectual protection. In this paper the platform goals and architecture are described, focusing on the recent achievement regarding the connection of distinct algorithms, which enables the execution of dynamic simulations using sequences of algorithms from distinct sources. A case study based on several SEAS SI available algorithms is presented with the objective of showing the advantages of the SEAS SI capability of supporting simulations based on sequences of algorithms. Namely, electricity market bid values are calculated by a metalearner, which is fed by market price forecasts using different methods, and by their respective forecasting errors. A case study presents some results to validate the presented work, through the simulation of the MIBEL electricity market using MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

关键词： Collaborative Simulations Electricity Markets Forecasting Metalearning Shared Intelligence

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Clustering Optimization of Distributed Energy Resources in Support of an Aggregator

Clustering Optimization of Distributed Energy Resources in S...

引用

IEEE Symposium Series on Computational Intelligence

作者： Joao Spinola Ricardo Faia Pedro Faria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development ISEP/IPP-Polytechnic of Porto Porto Portugal

Power systems have witnessed a gradual increase of the relevance and number of energy aggregators. Moreover, with the large-scale installation of distributed energy resources, energy aggregators integrate several features that can improve power system's operation. The present paper addresses this topic through a clustering optimization for the aggregator's scheduling. The costs of aggregated distributed energy resources are minimized through a group tariff, i.e. the same tariff is applied to all within a group. Given the context of aggregators participating in energy markets and the requirements that it involves, often compose issues for the aggregator that may prevent it from participating. The proposed algorithm guarantees the amount of energy and number of resources by group needed, by using metaheuristics combined with k-means clustering. The algorithm is tested in a day-ahead case study composed of 20,310 consumers, 548 distributed generators, and 10 retailers, of which, the obtained results show an improvement in the energy costs of the aggregator.

关键词： Aggregator Demand response Distributed generation Metaheuristics Clustering

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TOOCC: Enabling Heterogeneous Systems Interoperability in the Study of Energy Systems

TOOCC: Enabling Heterogeneous Systems Interoperability in th...

引用

IEEE Power and Energy Society General Meeting

作者： Brigida Teixeira Francisco Silva Tiago Pinto Gabriel Santos Isabel Praca Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering - Polytechnic of Porto (ISEP/IPP)

ISBN: (纸本)9781538622131;9781538622124

The environmental impact and the scarcity of limited fossil fuels led to the need of investment in energy based on renewable sources. This has driven Europe to implement several policies that changed the energy market's paradigm, namely the incentive to microgeneration. The penetration of energy sources from intermittent nature has increased the unpredictability of the system, which makes simulation and analysis tools essential in order to provide decision support to entities in this sector. This paper presents the Tools Control Center (TOOCC) as a solution to increase the interoperability between heterogeneous agent-based systems, in the energy field. The proposed approach acts as a facilitator in the interaction between different systems through the usage of ontologies, allowing them to communicate in the same language. To understand the real applicability of this tool, a case study is presented concerning the interaction between several systems, with the purpose of enabling the energy resource scheduling of a microgrid, and the reaction of a house managed by a house management system.

关键词： Tools Ontologies Microgrids Frequency modulation Electricity supply industry Load modeling Proposals

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Analyzing Electric Vehicle Charging Behaviour Using advanced Clustering Tools 20th

Analyzing Electric Vehicle Charging Behaviour Using Advanced...

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20th International Symposium on Distributed computing and Artificial Intelligence, DCAI 2023

作者： da Veiga, Carlos Ernani Ramos, Carlos Corchado, Juan Manuel Fernandes, Piara Soares, João DAE - Academic Department of Electrotechnics IFSC - Federal Institute of Santa Catarina Florianopolis Brazil GECAD - Research Group On Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Port Porto Portugal BISITE Research Group University of Salamanca Edificio I+D+I Salamanca37008 Spain

ISBN: (纸本)9783031383175

Electric vehicle (EV) owners often have electric vehicle charging stations (EVCS) in their homes and leave the EV connected when it remains in the garage. This procedure does not guarantee that EV charging is adequate, and monitoring this period is necessary to identify anomalies. A study was developed where the EV charging curves was analyzed by clustering with K-means. The advantage presented in this work is that the necessary data for the charging analysis come from the EVCS power supply circuit. This proposal allows monitoring and rapidly classifying the data according to the form of the EV charging curves, with no need for the supervision of the input data. The unsupervised machine learning method can classify any situation by collecting data on the electrical power supplied to the EVCS. In this study, the classification was applied with learning performed in the history, identifying the clusters representing abnormal situations to identify anomalies in future charging curves. The results of the proposed approach presented around 27.8% as an anomaly in the EV charging, allowing to identify the moment in which it occurs and to make the necessary adjustments to avoid them. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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