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检索条件"机构=GECAD - Research Group on Intelligent Eng. and Computing for Advanced Innovation and Development"

共 136 条记录，以下是81-90 订阅

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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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Enhance Demand Response Participants Performance Using a Personalized Recommendation Method

Enhance Demand Response Participants Performance Using a Per...

引用

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

作者： Cátia Silva Pedro Faria Zita Vale Intelligent Systems Associated Laboratory (LASI) Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Polytechnic of Porto Porto Portugal

For a successful implementation of Demand Response (DR) in the real markets, the uncertainty from the players' response must be reduced. From the community manager's perspective, the proposed methodology focuses on selecting trustworthy participants according to their availability for the triggered DR event. As innovation from previous works, the authors focus now on the consumer perspective. The present paper introduces a tool developed for performance enhancement by displaying the expected rewards according to consumer participation. In other words, the tool performs a sensitivity analysis to study all the possibilities and achieve the player's goal. For example, the tool might suggest the optimal one according to the frequency and type of DR programs triggered, but the participation is voluntary and depends on player availability. The case study results discuss the perspective of four players with distinct objectives.

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Production and Maintenance Scheduling for Total Cost and Machine Longevity Optimization

Production and Maintenance Scheduling for Total Cost and Mac...

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Future Energy Solutions (FES), International Conference on

作者： Bruno Mota Pedro Faria Bruno Canizes Carlos Ramos LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal

Production and maintenance scheduling for total cost-effective and machine longevity manufacturing is a key aspect in dealing with the ever-increasing energy prices, competitiveness, added maintenance costs, and environmental pressures that the manufacturing sector faces nowadays. This paper addresses these issues by proposing a novel intelligent production scheduling system for joint optimization of production and maintenance for overall cost minimization and machine longevity improvement. To achieve this, it is proposed a Genetic Algorithm (GA) for production and maintenance scheduling of flexible job shop manufacturing environments. The proposed GA takes into account volatile market energy prices, Renewable Energy Resources (RERs), surplus energy selling, maintenance activities, and constraints imposed on the production plan. A case study from the literature is used as a baseline scenario to validate the proposed scheduler. It uses real-production data and considers three unique machines with 275 tasks to be scheduled among them. Using the baseline scenario, it was possible to demonstrate the robustness of the proposed scheduler in reducing total costs, by taking advantage of the volatility of energy prices, as well as utilizing RERs to cover energy expenses or for selling excess energy. Furthermore, it highlights the ability to reduce the overload of single machines. Accordingly, it was possible to achieve cost reductions of up to 11.0% and improvements of 24.4% in machine longevity when compared to the baseline scenario.

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Analysing the Impact of Local Electricity Transactions with EV Penetration on the Electrical Grid

Analysing the Impact of Local Electricity Transactions with ...

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International Conference on European Electricity Market, EEM

作者： Fernando Lezama Ricardo Faia Pedro Faria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

This article analysis, through simulation, the impact that local transactions and the penetration of EVs have on the distribution network and the costs and incomes of local market participants. An auction-based competitive market and mathematical model are provided to simulate end-user transactions with EVs on the low-voltage grid. The proposed framework is validated in a scenario considering 55 end-users (some with PV generation and EVs) and six combined heat and power generators trading energy in the IEEE European Low Voltage Test Feeder system. Furthermore, a distribution system operator is considered by defining network constraints (voltage limits and lines overloading) that ensure the correct operation of the distribution grid.

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Optimizing House Energy Management with MILP Considering Shifting of Electric Appliances and Battery Storage System

Optimizing House Energy Management with MILP Considering Shi...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Ricardo Faia Pedro Faria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

The integration of renewable energy sources into the power grid has led to the need for efficient energy management systems that consider the presence of energy storage systems and controllable loads. In this paper, the authors propose a mixed-integer linear programming optimization model for energy management that takes into account the operation of shift electric appliances and battery storage systems. The proposed model aims to minimize the overall electricity cost while ensuring that the energy demand of the system is met, the battery state of charge is maintained within safe operating limits, and the shift electric appliance met with the user requirements. The authors consider the operational constraints of shifting electric appliances, which can be scheduled to operate during periods of low electricity prices. Additionally, they account for the energy storage system, which can be charged during periods of excess renewable energy production and discharged during periods of high demand. Four different scenarios are tested to prove the value of the model.

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Joint Optimization of Production and Maintenance for Effective Manufacturing Using a Genetic Algorithm

Joint Optimization of Production and Maintenance for Effecti...

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

作者： Bruno Mota Pedro Faria Carlos Ramos GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

While energy prices are seen as the major drive for competitiveness in the manufacturing field, intelligent maintenance scheduling is also one of the most widespread and significant issues plaguing the manufacturing industry. Maintenance activities can cost between 15% and 70% of the price of goods sold, thus it is essential to optimize both energy and maintenance costs. The purpose of this paper is to propose a production line optimization system that focuses on reducing the total costs (i.e., energy and maintenance costs) while also taking into account dynamic pricing, Renewable Energy Resource (RER) usage, and constraints applied in the production plan. In the proposed system, tasks and maintenance activities are scheduled by using a Genetic Algorithm. To validate the proposed system, a baseline scenario that uses real production data is considered. The obtained results show that the system is able to comply with imposed maintenance hours while also minimizing costs by shifting tasks to higher RER generation and lower energy price times, while the opposite is done to maintenance activities.

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Demand Response-based Energy Management Model for Energy Communities considering Data Privacy of the Members

Demand Response-based Energy Management Model for Energy Com...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Ruben Barreto Luis Gomes Pedro Faria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

This paper proposes a demand response-based energy management model for energy communities, considering the respective members’ data privacy. Through forecasting and clustering algorithms, this model can identify demand response opportunities for the next day, rank and select the participants for the event and monitor and evaluate the respective event. The paper’s novelty lies in increasing the energy community members’ active participation by allowing them to submit one or more proposals to participate in a demand response event. In this way, a member can, on the one hand, have more freedom to choose how to participate and, on the other hand, have more opportunities to contribute to the DR event with their energy flexibility. This model was tested with real data from an energy community with 50 buildings, which can provide flexibility through reductions and shifting. The results showed that the model’s efficiency could increase by considering the multiple proposals per member. Moreover, it was verified that with just one event, it is possible to reduce the CO 2 emissions and the energy cost by 50% and 73%, respectively, as well increasing the energy community sustainability by 10%.

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Automated energy management and learning

Automated energy management and learning

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Artificial Intelligence (CAI), IEEE Conference on

作者： Gabriel Santos Brígida Teixeira Tiago Pinto Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate LAboratory Polytechnic of Porto Porto Portugal

Automatic energy management systems allow users’ active participation in flexibility management while assuring their energy demands. We propose a transparent framework for automated energy management to increase trust and improve the learning process, combining machine learning, experts’ knowledge, and semantic reasoning. A practical example of thermal comfort shows the advantages of the framework.

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Multilevel Electricity Trading Simulation considering Energy Communities Participation

Multilevel Electricity Trading Simulation considering Energy...

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International Conference on European Electricity Market, EEM

作者： Gabriel Santos Ricardo Faia Fernando Lezama Zita Vale GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

The EU is encouraging the creation of local energy communities (LECs) for electricity trading, promoting local balance and a self-sustained community while reducing electricity bills. Local electricity markets (LEMs) ease the electricity trading of distributed energy resources while incentivizing the integration of renewable energy sources into the grid. However, presently, LEMs have low liquidity, and the demand is significantly higher than the supply. One possible solution to address this issue is participation in the wholesale market, assessing lower prices, and providing additional savings. This work proposes a multilevel electricity trading framework for LECs’ participation. The simulation framework comprises different LEM models for electricity trading at different levels, culminating in wholesale market participation through a LEC aggregator. Results show the benefits of LECs’ participation in the multilevel trading platform with significant savings.

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