ISEP – GECAD – Comunicações em eventos científicos
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- Radial Distribution Network Topology Optimization Using Genetic Algorithms Considering Uncertain Load and Distributed GenerationPublication . Ribeiro, Vitor Hugo; Azevedo, FilipeThis paper aims to study distribution network topology optimization considering uncertain load and distributed generation. Gradual increase of distributed generation in distribution network leads the network operator companies to concern more about having the best network topology, so their costs can be the lowest. MATLABTM genetic algorithms function is used to model this mathematical problem in its basic definition. A stochastic multi-objective programming algorithm is implemented and a decision maker applied to choose the best solution of non-dominated solutions set found.
- From Data to Action: Exploring AI and IoT-driven Solutions for Smarter CitiesPublication . Dias, Tiago; Fonseca, Tiago; Vitorino, João; Martins, Andreia; Malpique, Sofia; Praça, IsabelThe emergence of smart cities demands harnessing advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to unlock cities' potential to become more sustainable, efficient, and ultimately livable for their inhabitants. This work introduces an intelligent city management system that provides a data-driven approach to three use cases: (i) analyze traffic information to reduce the risk of traffic collisions and improve driver and pedestrian safety, (ii) identify when and where energy consumption can be reduced to improve cost savings, and (iii) detect maintenance issues like potholes in the city's roads and sidewalks, as well as the beginning of hazards like floods and fires. A case study in Aveiro City demonstrates the system's effectiveness in generating actionable insights that enhance security, energy efficiency, and sustainability, while highlighting the potential of AI and IoT-driven solutions for smart city development.
- Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness DetectionPublication . Vitorino, João; Rodrigues, Lourenço; Maia, Eva; Praça, Isabel; Lourenço, AndréDrowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times. To detect drowsiness before any impairment occurs, a promising strategy is using Machine Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work presents multiple experiments with different HRV time windows and ML models, a feature impact analysis using Shapley Additive Explanations (SHAP), and an adversarial robustness analysis to assess their reliability when processing faulty input data and perturbed HRV signals. The most reliable model was Extreme Gradient Boosting (XGB) and the optimal time window had between 120 and 150 s. Furthermore, the 18 most impactful features were selected and new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.
- LEMMAS: a secured and trusted Local Energy Market simulation systemPublication . Andrade, Rui; Vitorino, João; Wannous, Sinan; Maia, Eva; Praça, IsabelThe ever changing nature of the energy grid and the addition of novel systems such as the Local Energy Market (LEM) drastically increase its complexity, thus making the management harder and with increased importance at local level. Providing innovative and advanced management solutions is fundamental for the success of this new distributed energy grid paradigm. In this paper we extend Multi-Agent System (MAS) based simulation tool for LEMs called LEMMAS. A cyberattack detection model is developed and integrated in LEMMAS with the objective of preventing cyber-attacks from affecting the negotiations. This model is compared with the previous version which only analysed the trustworthiness of participants. The results show that the cyber-attack detection model drastically increases the security capabilities of LEMMAS.
- DSO Contract Market for Demand Response Using Evolutionary ComputationPublication . Lacerda, Eduardo; Lezama, Fernando; Soares, João; Vale, ZitaIn this article, a cost optimization problem in local energy markets is analyzed considering fixed-term flexibility contracts between the DSO and aggregators. The DSO procures flexibility while aggregators of different types (e.g., conventional demand response or thermo-load aggregators) offer the service. We solve the proposed model using evolutionary algorithms based on the well-known differential evolution (DE). First, a parameter-tuning analysis is done to assess the impact of the DE parameters on the quality of solutions to the problem. Later, after finding the best set of parameters for the "tuned" DE strategies, we compare their performance with other self-adaptive parameter algorithms, namely the HyDE, HyDE-DF, and vortex search algorithms. Results show that with the identification of the best set of parameters to be used for each strategy, the tuned DE versions lead to better results than the other tested EAs. Overall, the algorithms are able to find near-optimal solutions to the problem and can be considered an alternative solver for more complex instances of the model.
- A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market ProblemPublication . Vieira, Miguel; Faia, Ricardo; Lezama, Fernando; Canizes, Bruno; Vale, ZitaEnergy community markets have emerged to promote prosumers' active participation and empowerment in the electrical power system. These initiatives allow prosumers to transact electricity locally without an intermediary such as an aggregator. However, it is necessary to implement optimization methods that determine the best transactions within the energy community, obtaining the best solution under these models. Particle Swarm Optimization (PSO) fits this type of problem well because it allows reaching results in short optimization times. Furthermore, applying this metaheuristic to the problem is easy compared to other available optimization tools. In this work, we provide a sensitivity analysis of the impact of different parameters of PSO in solving an energy community market problem. As a result, the combination of parameters that lead to the best results is obtained, demonstrating the effectiveness of PSO solving different case studies.
- Energy Resource Scheduling Optimization for Smart Power Distribution Grids - Hour-Ahead HorizonPublication . Canizes, Bruno; Soares, João; Almeida, José; Paris, Wanderley; Vale, ZitaAs the use of renewable energy sources grows, the energy aggregator company plays an increasingly significant role in ensuring extremely flexible supply and demand, as requested by the smart grid architecture. This study presents a model for the problem of intraday energy resource scheduling (hour-ahead). The model is solved using the CPLEX solver and is developed as mixed integer linear programming. A distribution network with 180 buses located in Portugal considering high distributed energy resources penetration is used to demonstrate the application of the proposed model. The findings indicate how forecast errors and contractual restrictions with energy storage systems and electric car charging stations affect hour-ahead scheduling costs.
- Evolutionary Algorithms for Energy Scheduling under uncertainty considering Multiple AggregatorsPublication . Almeida, José; Soares, João; Canizes, Bruno; Lezama, Fernando; Fotouhi Ghazvini, Mohammad Ali; Vale, ZitaThe ever-increasing number of electric vehicles (EVs) circulating on the roads and renewable energy production to achieve carbon footprint reduction targets has brought many challenges to the electrical grid. The increasing integration of distributed energy resources (DER) in the grid is causing severe operational challenges, such as congestion and overloading for the grid. Active management of distribution network using the smart grid (SG) technologies and artificial intelligence (AI) techniques can support the grid's operation under such situations. Implementing evolutionary computational algorithms has become possible using SG technologies. This paper proposes an optimal day-ahead resource scheduling to minimize multiple aggregators' operational costs in a SG, considering a high DER penetration. The optimization is achieved considering three metaheuristics (DE, HyDE-DF, CUMDANCauchy++). Results show that CUMDANCauchy++ and HyDE-DF present the best overall results in comparison to the standard DE.
- A Statistical Analysis of Performance in the 2021 CEC-GECCO-PESGM Competition on Evolutionary Computation in the Energy DomainPublication . Lezama, Fernando; Soares, João; Canizes, Bruno; Vale, ZitaEvolutionary algorithms (EAs) have emerged as an efficient alternative to deal with real-world applications with high complexity. However, due to the stochastic nature of the results obtained using EAs, the design of benchmarks and competitions where such approaches can be evaluated and compared is attracting attention in the field. In the energy domain, the “2021 CEC-GECCO-PESGM Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications” provides a platform to test and compare new EAs to solve complex problems in the field. However, the metric used to rank the algorithms is based solely on the mean fitness value (related to the objective function value only), which does not give statistical significance to the performance of the algorithms. Thus, this paper presents a statistical analysis using the Wilcoxon pair-wise comparison to study the performance of algorithms with statistical grounds. Results suggest that, for track 1 of the competition, only the winner approach (first place) is significantly different and superior to the other algorithms; in contrast, the second place is already statistically comparable to some other contestants. For track 2, all the winner approaches (first, second, and third) are statistically different from each other and the rest of the contestants. This type of analysis is important to have a deeper understanding of the stochastic performance of algorithms.
- Case-based reasoning using expert systems to determine electricity reduction in residential buildingsPublication . Faia, Ricardo; Pinto, Tiago; Vale, Zita; Corchado, Juan ManuelCase-based reasoning enables solving new problems using past experience, by reusing solutions for past problems. The simplicity of this technique has made it very popular in several domains. However, the use of this type of approach to support decisions in the power and energy domain is still rather unexplored, especially regarding the flexibility of consumption in buildings in response to recent environmental concerns and consequent governmental policies that envisage the increase of energy efficiency. In order to determine the amount of consumption reduction that should be applied in a building, this article proposes a methodology that adapts the past results of similar cases in order to achieve a decision for the new case. A clustering methodology is used to identify the most similar previous cases, and an expert system is developed to refine the final solution after the combination of the similar cases results. The proposed CBR methodology is evaluated using a set of real data from a residential building. Results prove the advantages of the proposed methodology, demonstrating its applicability to enhance house energy management systems by determining the amount of reduction that should be applied in each moment, thus allowing such systems to carry out the reduction through the different loads of the building.