Urban load optimization based on agent-based model representation

Tese de mestrado integrado em Engenharia da Energia e do Ambiente, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, em 2018O sistema energético atravessará uma profunda transformação nos próximos anos à medida que a produção renovável distribuída, a flexibilidade no lado do consumo e as funcionalidades de SmartGrid são implementadas. Este processo, conduzido em grande parte pelas imposições causadas pelos efeitos das alterações climáticas, implica profundas transformações na produção e consumo de energia e torna a transição energética extremamente urgente. Simultaneamente, novos players, entidades e modelos de negócio têm emergido em quase todos os níveis da cadeia energética desde a produção, a transmissão, distribuição e comercialização até à gestão da rede elétrica, num movimento conduzido pelo processo de particionamento (unbundling) do sistema elétrico e pela exigência de um sistema mais descentralizado e horizontal. O efeito combinado desta nova paisagem energética torna possíveis novas funcionalidades e arquitecturas de sistema na mesma medida em que coloca enormes problemas de natureza física e matemática mas também enormes questões económicas, sociais e políticas que terão, necessariamente, de ser abordadas e resolvidas. A Gestão do Consumo é um termo abrangente que representa tanto os mecanismos de Resposta na Procura (Demand Response) ou a Gestão no Lado da Procura (Demand-Side Management) e que se impõe como um dos problemas actuais mais importantes em sistemas energéticos inteligentes caracterizados por altas penetrações renováveis e mecanismos de mercado. Para resolver estes problemas, um conjunto de métodos matemáticos e computacionais têm sido propostos nos últimos anos. Otimização distribuída e sistemas inteligentes, sistemas baseados em agentes de software e teoria de jogos encontram-se entre algumas das ferramentas usadas para otimizar o consumo de energia e determinar o agendamento e a alocação ótima de equipamentos e máquinas para consumidores residenciais, comerciais e industriais. Na sequência de trabalhos prévios disponíveis na literatura da especialidade, o presente trabalho propõe um modelo geral para abordar o problema da otimização de cargas através de arquitecturas e métodos baseados no paradigma dos Agentes. O trabalho começa por definir agentes em pontos críticos da rede elétrica e os seus processos internos de raciocínio representados por modelos de otimização matemática. Seguidamente as interações entre agentes são modeladas como um jogo de dois níveis (bi-level game) entre uma entidade gestora da rede e consumidores de energia tipificados de forma a coordenar o carregamento de diversos equipamentos, incluindo veículos elétricos, e determinar uma solução admissível para o sistema global. A funcionalidade geral do modelo proposto é demonstrada através da sua implementação em software proprietário e recorrendo a um conjunto de dados específicos. Está, então, pronto para ser complementado e refinado no futuro de forma a ser aplicado em problemas do mundo real, de grandes dimensões, mas também novas implementações em software open source de forma a ficar acessível a novos utilizadores.The energy system is expected to go through a phase change in coming years as distributed generation, demand flexibility and SmartGrid features gets implemented. The main driver for this process, climate change, imposes constraints on energy production and consumption making energy transition extremely urgent. Simultaneously, new players, entities and business models have emerged at almost all levels of the energy chain from production, transmission, distribution and commercialization down to power grid management driven by the unbundling process and the call for a more decentralized and horizontal energy system. The combined effect of this new energy landscape makes new system’s architectures and functionalities desirable and possible, but poses huge physical, mathematical, engineering, economic and political questions and problems that need to be tackled. Load Management is one broad term depicting Demand-Side Management and Demand Response mechanisms and is one of the pressing problems on smart energy systems. To solve them, a plethora of computational and mathematical methods have been proposed in recent years. Distributed optimization and intelligence, software agents, agent-based systems and game theory are among the tools used to optimize load consumption and determine optimal device scheduling for residential, commercial and industrial power consumers Following previous work found in literature, the present work proposes a general framework to treat the load optimization problem using agent-based architectures and models. We start by defining agents at critical points within the power grid as well as their internal reasoning process depicted by mathematical optimization models. We then proceed to model the cooperative interactions between agents as a Bi-level game between a grid entity and typified power consumers in order to coordinate the charging of several appliances and electrical vehicles and determine a feasible solution for the global system. We show the general functionality of the framework by implementing it in software and applying it to specific datasets. The framework is suitable for further refinement and development when applied to real world problems

Optimal Home Energy Management System for Committed Power Exchange Considering Renewable Generations

This thesis addresses the complexity of SH operation and local renewable resources optimum sizing. The effect of different criteria and components of SH on the size of renewable resources and cost of electricity is investigated. Operation of SH with the optimum size of renewable resources is evaluated to study SH annual cost. The effectiveness of SH with committed exchange power functionality is studied for minimizing cost while responding to DR programs

Demand response for smart homes

RÉSUMÉ: Problèmes dans l’opération de la transmission d’électricité, surcharge, émission de carbone sont, entre autres, les préoccupations des gestionnaires de réseaux électriques partout dans le monde. Dans ce contexte, face au besoin de réduire les coûts d’exploitation ainsi que le besoin d’adaptation aux différentes exigences de qualité, de sécurité, de flexibilité et de durabilité, les réseaux intelligents sont considérés comme une révolution technologique dans le secteur de l’énergie électrique. Cette transformation sera nécessaire pour atteindre les objectifs environnementaux, intégrer la participation de la demande, appuyer l’adoption de véhicules électriques et hybrides ainsi que la production distribuée à basse tension. Chaque partie prenante dans le processus de gestion de l’énergie peut avoir des avantages avec le réseau intelligent, ce qui justifie son importance dans l’actualité. Dans ce travail, on se concentre plutôt sur l’utilisateur final. En plus de l’utilisateur final, nous utilisons également l’agrégateur, qui est une entité qui agrège un ensemble d’utilisateurs de sorte que l’union de leurs participations individuelles devienne plus représentative pour les décisions relatives au système d’énergie. La fonction de l’agrégateur est d’établir un engagement d’intérêts entre les utilisateurs finaux et l’entreprise de génération afin de satisfaire les deux parties. L’une des contributions principales de cette thèse est la mise au point d’une méthode qui donne à un agrégateur la possibilité de coordonner la consommation d’un ensemble d’utilisateurs, en maintenant le niveau de confort souhaité pour chacun d’entre eux et en les encourageant via des incitations monétaires à changer ses consommations, de sorte que la charge globale ait le coût minimal pour le producteur. Dans la première contribution (chapitre 4), ce travail se concentre sur le développement d’un modèle mathématique représentatif pour la planification des équipements d’un utilisateur. Le modèle intègre des modèles détaillés et fiables pour des équipements spécifiques tout en conservant une complexité telle que les solveurs commerciaux puissent résoudre le problème en quelques secondes. Notre modèle peut donner des résultats qui, comparés aux modèles les plus proches de la littérature, permettent des économies de coûts allant de 8% à 389% sur un horizon de 24 heures. Dans la deuxième contribution (chapitre 5), l’accent a été mis sur la création d’un cadre algorithimique destiné à aider un utilisateur final particulier dans son processus de décision lié à la récupération d’investissement sur l’acquisition d’appareils ou d’équipements (composants) intelligents. Pour un utilisateur spécifique, le cadre analyse différentes combinaisons de composants intelligents afin de déterminer lequel est le plus rentable et à quel moment il convient de l’installer. Ce cadre peut être utilisé pour encourager un utilisateur à adopter un concept de maison intelligente réduisant les risques liés à son investissement. La troisième contribution(chapitre 6) regroupe plusieurs maisons intelligentes. Un cadre algorithimique basé sur les programmes de réponse à la demande est proposé. Il utilise les résultats des deux contributions précédentes pour représenter plusieurs utilisateurs, et son objectif est de maximiser le bien-être social, en tenant compte de la réduction des coûts pour un producteur donné ainsi que de la satisfaction de chaque consommateur. Les résultats montrent que, du point de vue du producteur, la courbe de charge globale est aplatie sans que cela ait un impact négatif sur le confort des utilisateurs ou sur leurs coûts. Enfin, les expériences rapportées dans chaque contribution valident théoriquement l’efficacité des approches proposées.----------ABSTRACT: Transmission operation issues, overload, carbon emissions are, among others, the concerns of power system operators worldwide. In this context, faced with the need to reduce operating costs and the need to adapt to the different requirements of quality, security, flexibility and sustainability, smart grids are seen as a technological revolution in the field of power system. This transformation will be necessary to achieve environmental objectives, support the adoption of electric and hybrid vehicles, improve distributed low-voltage generation and integrate demand participation. Each stakeholder in the energy management process can have advantages with the smart grid, which justifies its current importance. The focus of this thesis is rather on the end user. In addition to the end-user, this work also uses the aggregator that is an entity that aggregates a set of users such that the union of the individual participation of each user becomes more representative for power system decisions. The function of the aggregator is to establish an engagement of interests between the end users and the generator company in order to satisfy both parties. One of the main contributions of this thesis is the development of a method that gives an aggregator the possibility to coordinate the consumption of a set of users, keeping the desired comfort level for each of them and encouraging them via monetary incentives to change their consumption such that their aggregated load has the minimal cost for the generator company. In the first contribution (Chapter 4), this work focuses on developing a representative mathematical model for user appliances scheduling. The model integrates detailed and reliable models for specific appliances while keeping a complexity such that commercial solvers are able to solve the problem in seconds. Our model can give results that, compared to the closest models in the literature, provide a cost savings in the range of 8% and 389% over a scheduling horizon of 24 hours. In the second contribution (Chapter 5), the focus was given in making a framework to help a specific end-user in their decision process related to the payback for an acquisition of smart appliances or equipment (components). For a specific user, the framework analyses various combinations of smart components to discover which one is the most profitable and when it should be installed. This framework can be used to encourage users towards a smart home concept decreasing the risks about their investment. The third contribution (Chapter 6) aggregates several smart homes. A framework based on demand response programs is proposed. It uses outputs from the two previous contributions to represent multiple users, and its goal is to maximize the social welfare, considering the reduction of costs for a given generator company as well the satisfaction of every user. Results show that, from the generator company perspective, the aggregate load consumption is flattened without impacting negatively the users’ comfort or their costs. Finally, the experiments reported in each contribution validate, in theory, the efficiency of the proposed approaches

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes