Study and analysis of the use of flexibility in local electricity markets

In this work an introduction to Local Electricity Markets (LEM) was done and afterwards evolutionary algorithms (EAs) such as Differential Evolution (DE), HybridAdaptive Differential Evolution (HyDE), Hybrid-Adaptive Differential Evolution with Decay Function (HyDE-DF) and Vortex Search (VS) were applied to a market model in order to test its efficiency and scalability. Then, the market model was expanded adding a network model from the BISITE laboratory and again tests using the evolutionary algorithms were performed. In more detail, first a literature review is done about distributed generation, load flexibility, LEM and EAs. Then a cost optimization problem in Local Electricity Markets is analyzed considering fixed-term flexibility contracts between the distribution system operator (DSO) and aggregators. In this market structure, the DSO procures flexibility while aggregators of different types (e.g., conventional demand response or thermo-load aggregators) offer the service. Its then solved 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 VS. 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. After this a network model, from BISITE laboratory, is added to the problem and new analyses are performed using evolutionary algorithms along with MATPOWER power flow algorithms. Results show that evolutionary algorithms support from simple to complex problems, that is, it is a scalable algorithm, and with these results it is possible to perform analyses of the proposed market model.Neste trabalho foi feita uma introdução aos Mercados Locais de Eletricidade (MLE) e posteriormente foram aplicados algoritmos evolutivos (AEs) como Differential Evolution (DE), Hybrid-Adaptive Differential Evolution (HyDE), Hybrid-Adaptive Differential Evolution with Decay Function (HyDE-DF) e Vortex Search (VS) a um modelo de mercado a fim de testar a sua eficiência e escalabilidade. O modelo de mercado foi expandido adicionando uma rede do laboratório BISITE e novamente foram realizados testes usando os algoritmos evolutivos. Em mais detalhe, no trabalho primeiro foi feita uma revisão bibliográfica sobre geração distribuída, flexibilidade de carga, MLE e AEs. É analisado um problema de optimização de custos nos MLE, considerando contratos de flexibilidade a prazo fixo entre os agentes. O distribuidor adquire flexibilidade enquanto que os agregadores de diferentes tipos (por exemplo, os agregadores convencionais de resposta à procura ou de carga térmica) oferecem o serviço. Resolve-se depois o modelo proposto utilizando AEs baseados na conhecida DE. É feita uma análise de afinação de parâmetros para avaliar o impacto dos parâmetros DE na qualidade das soluções para o problema. Após encontrarmos o melhor conjunto de parâmetros para as estratégias DE "afinadas", comparamos o seu desempenho com outros algoritmos de parâmetros autoadaptáveis, nomeadamente o HyDE, HyDE-DF, e VS. Globalmente, os algoritmos são capazes de encontrar soluções quase óptimas para o problema e podem ser considerados um solucionador alternativo para instâncias mais complexas do modelo. Então um modelo de rede, do laboratório BISITE, é acrescentado ao problema e novas análises são realizadas utilizando algoritmos evolutivos juntamente com algoritmos de fluxo de potência MATPOWER. Os resultados mostram que os algoritmos evolutivos suportam desde problemas simples a complexos, ou seja, é um algoritmo escalável, e com estes resultados é possível realizar análises do modelo de mercado proposto

The Dynamics of Multi-Modal Networks

The widespread study of networks in diverse domains, including social, technological, and scientific settings, has increased the interest in statistical and machine learning techniques for network analysis. Many of these networks are complex, involving more than one kind of entity, and multiple relationship types, both changing over time. While there have been many network analysis methods proposed for problems such as network evolution, community detection, information diffusion and opinion leader identification, the majority of these methods assume a single entity type, a single edge type and often no temporal dynamics. One of the main shortcomings of these traditional techniques is their inadequacy for capturing higher-order dependencies often present in real, complex networks. To address these shortcomings, I focus on analysis and inference in dynamic, multi-modal, multi-relational networks, containing multiple entity types (such as people, social groups, organizations, locations, etc.), and different relationship types (such as friendship, membership, affiliation, etc.). An example from social network theory is a network describing users, organizations and interest groups, where users have different types of ties among each other, such as friendship, family ties, etc., as well as affiliation and membership links with organizations and interest groups. By considering the complex structure of these networks rather than limiting the analysis to a single entity or relationship type, I show how we can build richer predictive models that provide better understanding of the network dynamics, and thus result in better quality predictions. In the first part of my dissertation, I address the problems of network evolution and clustering. For network evolution, I describe methods for modeling the interactions between different modalities, and propose a co-evolution model for social and affiliation networks. I then move to the problem of network clustering, where I propose a novel algorithm for clustering multi-modal, multi-relational data. The second part of my dissertation focuses on the temporal dynamics of interactions in complex networks, from both user-level and network-level perspectives. For the user-centric approach, I analyze the dynamics of user relationships with other entity types, proposing a measure of the "loyalty" a user shows for a given group or topic, based on her temporal interaction pattern. I then move to macroscopic-level approaches for analyzing the dynamic processes that occur on a network scale. I propose a new differential adaptive diffusion model for incorporating diversity and trust in the process of information diffusion on multi-modal, multi-relational networks. I also discuss the implications of the proposed diffusion model on designing new strategies for viral marketing and influential detection. I validate all the proposed methods on several real-world networks from multiple domains

Differential evolution with an evolution path: a DEEP evolutionary algorithm

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs

Stochastic oscillations of adaptive networks: application to epidemic modelling

Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can radically alter the system's behaviour. In this article we develop a method to predict the effects of stochasticity in adaptive networks by making use of a pair-based proxy model. The technique is developed in the context of an epidemiological model of a disease spreading over an adaptive network of infectious contact. Our analysis reveals that in this model the structure of the network exhibits stochastic oscillations in response to fluctuations in the disease dynamic.Comment: 11 pages, 4 figure