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Scalable computational framework using intelligent optimization: Microgrids dispatch and electricity market joint simulation

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IFAC-PapersOnLine 2017年第1期50卷 3362-3367页

作者： Soares J. Pinto T. Sousa F. Borges N. Vale Z. Michiorri A. GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation Development Polytechnic of Porto (IPP) Porto Portugal MINES Paristech France

Worldwide microgrid capacity is expected to reach 7 GW and a market value of $35 billion dollars in the next few years. The decentralization of the generation dispatch role and different ownership models concerning microgrids, will contribute to increase the complexity of the future power systems. Analyzing new policies and strategies as well as evaluating those impacts is only possible with the use of sophisticated simulation tools. This paper presents a scalable computational simulation to address microgrid dispatch and the impact in the electricity market. Computational intelligence techniques are integrated to improve the effectiveness of the simulation tool. These techniques include CPLEX; differential search algorithm and quantum particle swarm optimization. Each microgrid player is able to solve a day-ahead scheduling problem and submit bids to the electricity market agent (spot market), which calculates the market clearing price. The developed case study with a large number of players totaling about 150,000 consumers suggest the relevance of the developed computational framework. © 2017

关键词： agent-based demand response market microgrid simulation stochastic optimization

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Demand Response-based Model for an Energy Community Considering Members and Electric Vehicles Participation

Demand Response-based Model for an Energy Community Consider...

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2023 IEEE International Conference on Environment and Electrical eng.neering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Barreto, Ruben Gomes, Luis Vale, Zita Polytechnic of Porto GECAD - Res. Grp. on Intelligent Eng. and Computing for Advanced Innovation and DevelopmentLASI Porto Portugal

ISBN: (纸本)9798350347432

This paper proposes a demand response-based model to manage the energy resources of an energy community, considering the data privacy of the respective community members and electric vehicles. First, through a day-ahead forecasting algorithm, the implemented model identifies possible demand response events to be launched in the energy community. Then, the community members and electric vehicles are ranked to identify which will be selected to participate in the respective demand response event. This selection is based on several metrics created to assess different types of community members' data through clustering algorithms. Furthermore, this model also considers real-time monitoring and evaluation of the demand response event. Finally, the implemented model was tested with energy community data containing 750 possible candidates to participate in the demand response event with their flexibility, where these candidates are households and several different electric vehicles. The results show that the implemented model enables a saving of 13.66 EUR while reducing the CO2 emissions by 32.34 kg in a single demand response event. © 2023 IEEE.

关键词： Data privacy

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Inverse Neural Control of a Magnetic Levitation System: Experimental Results 20th

Inverse Neural Control of a Magnetic Levitation System: Expe...

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20th International Conference on Hybrid intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Silva, Bruno E. Barbosa, Ramiro S. Porto Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development ISEP/IPP Porto Portugal

ISBN: (纸本)9783030730499

In this paper we investigate the use of inverse neural models to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The neural controllers are tested and analyzed on a low-cost Arduino control platform using MATLAB/Simulink. Experimental results show the effectiveness of neural networks to control nonlinear systems with better performance than conventional algorithms. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Costs

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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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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

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

ISBN: (纸本)9798350396782

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. © 2023 IEEE.

关键词： Battery storage systems Minimize Costs Shift appliances

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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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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

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

ISBN: (纸本)9798350396782

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 CO2 emissions and the energy cost by 50% and 73%, respectively, as well increasing the energy community sustainability by 10%. © 2023 IEEE.

关键词： Active Participation Clustering Demand Response Energy Community Energy Flexibility

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A logic-based flexibility services tool to support a rich renewable distribution network operation

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Sustainable Energy, Grids and Networks 2025年 43卷

作者： Bruno Canizes Fábio Castro Vitor Silveira Zita Vale Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto 4200-072 Portugal LASI – Intelligent Systems Associate Laboratory 431 Porto 4200-072 Portugal GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto 4200-072 Portugal

In recent years, the low-voltage power distribution networks and overall power systems have been undergoing substantial transformations. What were once mere innovation trends are now solidifying into the new norm, driven by advancements in technology and the decreasing costs of manufacturing. Moreover, the role of distribution system operators has been elevated with the widespread deployment of smart meters, coupled with a growing emphasis on eng.ging citizens as pivotal contributors in shaping the future of energy markets and system operations. This evolving topic underscores the importance of devising innovative approaches to explore potential mechanisms for offering additional services within distribution networks, particularly at low-voltage levels. This paper presents a novel logic-based heuristic approach aimed at tackling voltage and congestion challeng.s in low-voltage distribution networks. The approach revolves around bolstering the involvement of small-scale users in demand response initiatives and enhancing the flexibility of dispatchable distributed renewable energy sources to serve as flexibility services. To showcase the efficacy of the proposed model, a case study was conducted on a 236-bus low-voltage distribution network, chosen to reflect real-world conditions. The results demonstrate a substantial improvement in voltage profiles and a noteworthy reduction in congestion levels, when comparing the scenarios pre- versus post-optimization, and flexibility versus no flexibility, validating the effectiveness of the approach.

关键词： Congestion management Demand flexibility Dispatchable renewable generation flexibility Low-voltage distribution network Voltage profile

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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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Detection of Human Falls via Computer Vision for Elderly Care – An I3D/RNN Approach 1

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

作者： Leal, João Moayyed, Hamed Vale, Zita 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

ISBN: (数字)9783031383335

ISBN: (纸本)9783031383328

As the population continues to age, the number of elderly individuals is increasing at an unprecedented rate as the active age group in developed countries continues to shrink. This demographic shift has resulted in a shortage of resources and limitations in the provision of adequate elderly care. Among the many health risks faced by the elderly, falls are a common and serious problem, which can lead to injuries, hospitalizations, and even death. Despite the prevalence of falls, traditional manual detection methods are often unreliable and inadequate. In response to this, this paper proposes an automatic fall detection system based on video cameras that uses a unique combination of an inflated 3D convolutional neural network (I3D) and a recurrent neural network (RNN). This model was evaluated using multiple fall detection datasets, including a newly developed dataset of simulated falls. The results of this study demonstrate that this hybrid model is highly effective at detecting falls and is on par with other state-of-the-art systems, boasting 94% F1 score and 96% recall values. This technology has the potential to significantly improve the quality of life and safety of the elderly population by providing timely and accurate detection of falls. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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Using simulation to evaluate a concept drift detector for condition based maintenance 48

Using simulation to evaluate a concept drift detector for co...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Lourenco, Afonso Fernandes, Marta Marreiros, Goreti Corchado, Juan Manuel Gecad-Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal Bisite-Research Group on Bioinformatics Intelligent Systems and Educational Technology Salamanca Spain

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Industry 4.0. has empowered the integration of real-time sensor readings and process knowledge for early identification of faults. In this paper, simulation was used to evaluate the process of classifying data streams as drifting towards defective behavior. Focusing on the challeng.s imposed by real-word, unlabeled and non-stationary data, the proposed method was assessed by conducting a case-study in the plastic extrusion domain. The knowledge gained from initial tests on generated synthetic data sets was used to isolate symptoms of a clogged screen pack from the concept drifts detected by the Adaptive Windowing algorithm. Experimental results for 40 days of running time of an extruder show that the proposed method helped in identifying a defective screen pack state upon the appearance of defects in the extrudate. © 2022 IEEE.

关键词： Industry 4.0

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A Robust Optimization for Day-ahead Microgrid Dispatch Considering Uncertainties

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IFAC-PapersOnLine 2017年第1期50卷 3350-3355页

作者： Borges N. Soares J. Vale Z. GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/IPP) Rua Dr. Almeida 431 Porto 4200-072 Portugal

This paper presents a Particle Swarm Optimization (PSO) methodology to solve the problem of day-ahead microgrid (MG) dispatch with high penetration of Distributed Generation (DG) and considering uncertainties. The proposed methodology has the objective to satisfy demand aiming at obtaining the maximum profit, corresponding to the difference between the income and costs of the MG. This methodology considers the uncertainties associated with the production of electricity by the photovoltaic and wind sources. This uncertainty is modeled with the use of a robust approach in PSO. A case study is presented using a 21-bus MG from a real university campus in Portugal, and the projection of distributed energy resources based on the evolution scenario for the year 2050 managed by an aggregator. © 2017

关键词： Energy Resources Management Microgrids Particle Swarm Optimization Robust Optimization

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