Automatic Selection of Optimization Algorithms for Energy Resource Scheduling using a Case-Based Reasoning System

This paper proposes a case-based reasoning methodology to automatically choose the most appropriate optimization algorithms and respective parameterizations to solve the problem of optimal resource scheduling in smart energy grids. The optimal resource scheduling is, however, a heavy computation problem, which deals with a large number of variables. Moreover, depending on the time horizon of this optimization, fast response times are usually required, which makes it impossible to apply traditional exact optimization methods. For this reason, the application of metaheuristic methods is the natural solution, providing near-optimal solutions in a much faster execution time. Choosing which optimization approaches to apply in each time is the focus of this work, considering the requirements for each problem and the information of previous executions. A case-based reasoning methodology is proposed, considering previous cases of execution of different optimization approaches for different problems. A fuzzy logic approach is used to adapt the solutions considering the balance between execution time and quality of resultsThis work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 (project DREAM-GO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013info:eu-repo/semantics/publishedVersio

Modelo de gestão de flexibilidade de equipamentos domésticos para auxiliar solicitações do DSO em redes inteligentes

A flexibilidade energética desempenhará um papel fundamental no funcionamento dos sistemas energéticos, introduzindo um conjunto de benefícios a todas as partes interessadas envolvidas e mudando o mercado energético como o conhecemos. Espera-se que novas entidades com diferentes interesses emerjam nesse contexto, particularmente os agregadores, que permitirão que os utilizadores finais estejam cientes do valor da flexibilidade no seu consumo, ou simplesmente facilitar a participação destes, por exemplo, através do uso de Demand Response (DR) no mercado, para atender às necessidades do sistema. No entanto, para retirar o maior proveito possível da flexibilidade adquirida de pequenos clientes, uma resposta rápida do sistema será necessária, permitindo a interação entre agregadores e utilizadores residenciais. Portanto, o chamado sistema de gestão de energia residencial torna-se uma ferramenta essencial para a comunicação entre utilizadores finais e agregadores, realizando mudanças necessárias nos perfis de consumo em benefício de todas as partes envolvidas. Nesta dissertação é introduzido um modelo com o objetivo de conseguir uma correspondência entre a flexibilidade exigida por um agregador e a flexibilidade oferecida pelos utilizadores residenciais através da capacidade de um sistema de gestão de energia residencial de realizar um conjunto de tarefas que são abordadas com maior detalhe ao longo da dissertação. Este modelo é desenvolvido usando a ferramenta informática R, que com determinados dados de entrada, devolve parâmetros otimizados usando a otimização por enxame de partículas (PSO). A ferramenta considera uma minimização de uma função objetivo com os seguintes termos: custo dos diferentes tipos de equipamentos em estudo, flexibilidade oferecida pelo sistema de gestão residencial e flexibilidade requerida pelo agregador. Assim sendo, a metodologia apresentada é aplicada a um conjunto de utilizadores residenciais de forma a testar a sua eficácia e eficiência. A análise dos resultados comprova a viabilidade, a adequação, robustez e versatilidade da metodologia proposta para esta dissertação.Energy flexibility will play a key role in the proper functioning of energy systems, introducing a set of benefits to all involved stakeholders and changing the shape of electricity markets as we know them. It is expected that new players with different interests will emerge in this context. Particularly, the aggregators might allow end-users to be aware of their consumption flexibility value, or merely facilitate consumer’s participation, for instance through the use of demand response in the market to meet system requirements. However, to take full advantage of the flexibility procurement of small customers, a prompt system response allowing the interaction between aggregators and residential users is needed. Therefore, the so-called Home Energy Management System (HEMS) becomes an active tool to communicate end-users with aggregators, performing the necessary changes in the consumption profiles in the benefit of all involved parts. In this dissertation, a model is introduced to match the flexibility required by an aggregator to the flexibility offered by residential users through the ability of a HEMS to perform a set of tasks that are addressed with detail throughout the dissertation. This model is developed using the R software, which with certain input data returns optimized parameters using particle swarm optimization (PSO). The tool considers a minimization of an objective function with the following terms: cost of the different types of equipment under study, flexibility offered by the residential management system and flexibility required by the aggregator. Thus, the presented methodology is applied to a set of residential users in order to test its effectiveness and efficiency. The analysis of the results, prove the feasibility, adequacy, robustness and versatility of the methodology proposed for this dissertation

Zastosowanie procesów epigenetycznych w algorytmach genetycznych

The main purpose of the dissertation is to develop and verify the possibility of application mechanism based on epigenetics processes in genetics algorithms. In the dissertation three new operations in genetic algorithms were introduced. Epigenetics is the study of heritable phenotype changes that do not involve changes in the DNA sequence. The epigenetics processes was discovered later than the genetic algorithms was develop. Therefore, the epigenetic processes have not been included in classical genetic algorithms. In the dissertation the operations mimics the following epigenetic processes: prion inheritance, methylation of cytosine and allelic exclusion. The mentioned operations have been tested in terms of the impact on the efficiency of genetic algorithms, evaluating the reduction of the number of generations needed to obtain the expected result by the genetic algorithm, and the impact on the operation time of the algorithm. Research was carried out on four selected genetic algorithms: loading optimization, data grouping, outliers detection and biological sequence alignment. It was proved, based on the conducted research, that the application of each proposed operation in all algorithms used in the experiments influenced the reduction of the number of generations needed to obtain the expected result by algorithms. The following contents were included in the work: • description of optimization problems, • literature review on evolutionary algorithms, • the idea and operating principle of genetic algorithms were discussed, • literature review of existing modifications of genetic algorithms had been made, • discussed selected genetic algorithms, algorithms that were used as a algorithms of reference in the experiments, the results of which were included in the work, • the idea and method of implementation of proposed operations inspired by epigenetic processes been shown • the result of the experiments on the assessment of the impact of the proposed operations on the genetic algorithms was presented. Based on the results of the experiments presented in the work, the thesis of the dissertation has been proved

Güncel en iyileme algoritmalarının paralel ve birlikte uygulamaları ve performans analizleri

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.En iyileme yöntemleri yapılan işin en iyi yapılmasını sağlamak için kullanılırlar. Bu tekniklerin kullanılmasındaki temel hedef her zaman için en iyi çözümleri yakalayabilmektir. Uygunluk veya hata değeri tanımlanabilen her sistemin en iyi çözümünün elde edilmesinde en iyileme algoritmaları kullanılabilir. Sadece ait oldukları problemlere özgü olmaları ve yüksek hesaplama maliyeti içermeleri gibi sebepler nedeniyle mevcut geleneksel en iyileme algoritmalarının kullanımı çok sayıda parametre içeren gerçek dünya problemlerinin çözümünde bazen yeterli olmayabilir. Bu gibi durumlarda daha az işlem ile daha kısa sürede en iyi çözüme yakınsayabilen meta-sezgisel yöntemlerin kullanımı daha makul çözümler olarak karşımıza çıkmaktadır. Son 20 yıl içerisinde doğadan ilham alınarak çok sayıda meta-sezgisel en iyileme algoritması geliştirilmiştir. Buna paralel olarak bazı araştırmacılar mevcut algoritmalar üzerinde birtakım iyileştirmeler yapmışlar, bazıları da birden fazla algoritmayı bir arada kullanarak performansı daha yüksek melez yöntemler elde etmişler ve daha sonra bu yöntemleri kullanarak gerçek dünya problemlerine en iyi çözümler üretmişlerdir. Bu tez çalışmasında sistem kimliklendirme süreci, yapay sinir ağı eğitimi, sempozyum katılımcı listelerinin düzenlenmesi, slab kesme uzunluklarının planlanması gibi gerçek dünyaya ait problemlere birer en iyileme problemi olarak yaklaşılmış, seçilen güncel ve yaygın meta-sezgisel algoritmalar kullanılarak geleneksel yöntemlerin çözümleri ile rekabet edebilen çözümler üretilmiştir. Ayrıca, karar ağacı tasarım süreci hem kombinatoryal hem de nümerik en iyilemeleri içeren bir problem olarak ele alınmış, olası karar ağacı tasarımları arasında sistematik arama yapan yeni bir yöntem ile karar ağacı tasarımı gerçekleştirilmiştir. Önerilen yöntemle elde edilen test sonuçlarının aynı veri setinin kullanıldığı daha önceki karar ağacı çalışmaları ile elde edilen sonuçlardan daha iyi olduğu görülmüştür. Son olarak, yapay arı koloni ve göçmen kuşlar en iyileme algoritmaları kullanılarak yeni modifiye, melez ve paralel çalışma sistematikleri önerilmiştir. Önerilen yöntemlerin performans testlerinden elde edilen sonuçlar, onların daha iyi keşif ve yakınsama yeteneklerine sahip olduklarını ortaya koymuştur.Optimization methods are employed in order to make a job in an optimal way. The main aim of their usage is to get an optimal solution in every execution. Optimization algorithms can be applied to find optimal solutions for the systems whose fitness or error calculations can be defined. Sometimes, existing conventional optimization algorithms may be insufficient for the real world problems having many parameters because of the reason that they are problem specific and have higher calculation costs. Since metaheuristic algorithms can find near optimal solutions with less calculations requiring lower time, their usages seem more feasible for these cases. Within the past 20 years, so many metaheuristic algorithms which are inspired by the nature have been developed by researchers. In parallel to these studies, while some of the researchers were working on some enhancements for existing algorithms, some of them were working on their hybrid forms. Then, they tried to find more optimal solutions for real world problems by using these new enhanced and hybrid algorithms. In this dissertation study, some real world problems such as system identification process, artificial neural network training, preparation of symposium attendee lists, scheduling slab cutting lengths etc. are thought to be optimization problems. Some competitive solutions with respect to solutions of the conventional methods are generated to these real world problems by using some recent and common metaheuristic algorithms. In addition, thinking the decision tree construction process as a problem including both numerical and combinatorial optimizations, a novel decision tree construction method which makes a systematic search among possible decision tree designs is proposed to get optimal decision tree. It is seen that the results obtained by proposed method are better than those of previous studies using same data set. Finally, some modified, hybrid and parallel running strategies using artificial bee colony and migrating birds optimization algorithms are proposed. It is observed from the performance test results that proposed strategies have better exploration and exploitation capabilities