Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization Algorithm

The recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.

Optimization of Generation and Aggregated Consumption Shifting for Demand Response Programs Definition

The concept of demand response has drawing attention to the active participation in the economic operation of power systems, namely in the context of recent electricity markets and smart grid models and implementations. In these competitive contexts, aggregators are necessary in order to make possible the participation of small size consumers and generation units. The methodology proposed in the present paper aims to address the demand shifting between periods, considering multi-period demand response events. The focus is given to the impact in the subsequent periods. A Virtual Power Player operates the network, aggregating the available resources, and minimizing the operation costs. The illustrative case study included is based on a scenario of 218 consumers including generation sources.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

Ferramentas para optimização da solução NGIN da PT Inovação

Mestrado em Engenharia Informática

RepComp - replicated software components for improved performance

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Concepção de um aparelho de medição 3D da coluna vertebral: Desenvolvimento de um software de detecção de apófises espinhosas e cálculo da distância ântero-posterior

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Biomédica

Análise da evolução de software com séries temporais

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Desenvolvimento e aluguer de software de faturação

As aplicações de Gestão ou Faturação são uma presença indispensável hoje em dia. Tendo o seu início nas aplicações “MS-DOS” em modo de texto, estas aplicações acompanharam a evolução dos sistemas operativos adotando um ambiente gráfico de forma natural. Se há poucos anos apenas as empresas com volumes de negócio significativo possuíam software de faturação, este foi sendo adotado por cada vez mais empresas e pequenos negócios. As alterações legislativas introduzidas desde 2011 conduziram a uma adoção generalizada por parte de pequenas e microempresas. O mercado de aplicações de gestão está saturado pelos grandes produtores de software nacionais: Primavera, Sage, etc. Estas aplicações, tendo sido construídas para PMEs (Pequenas e Médias Empresas) e mesmo grandes empresas, são excessivamente complexas e onerosas para muito pequenas e microempresas. O Modelo de negócio destes produtores de software é primordialmente a venda de Licenças e contratos de Manutenção, nalguns casos através de redes de Agentes. Este projeto teve como objetivo o desenvolvimento de uma Aplicação de Faturação, de baixo custo, simples e cross-platform para ser comercializada em regime de aluguer em Pequenas e Micro Empresas.

Modelação de um Sistema de AVAC, em Software LabVIEW

Na União Europeia, a energia utilizada nos edifícios é responsável por uma grande parte do consumo total, cerca de 40%, de toda a energia produzida, contribuindo em grande escala para as emissões de gases de efeito de estufa, como o CO2. [ADENE, 2014]. A minimização deste consumo, durante o período de ciclo de vida de um edifício, é um grande desafio associado ao ambiente e à economia. Na atualidade assistimos, cada vez mais, ao emergir de novas tecnologias. Faz parte dessa realidade, o crescimento e o desenvolvimento das UTA’s, que surgem como resposta do ser humano pela busca de otimização da sua zona de conforto, da qualidade de ar interior e da eficiência energética. Assim, para que não se sacrifique o conforto térmico, há que conciliar a qualidade de ar interior com a energia dispensada para climatizar os espaços. Para ajudar à minimização de CO2 em conjunto com uma eficiência energética e conforto térmico, traduzindo-se numa melhor qualidade de ar no interior de espaços climatizados, surge o objetivo de implementar uma aplicação através do software LabVIEW para prever uma experiência real. Como solução, recorreu-se a modelos matemáticos que traduzissem os vários balanços térmicos, balanços de massa e de CO2. As principais conclusões deste trabalho foram: validação do comportamento do modelo matemático da temperatura; validação do comportamento do modelo matemático de CO2; humidade relativa com 25% de registos válidos.

Extremum estimators and stochastic optimization methods

Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia

Invited review: Models for the optimization of regional wastewater treatment systems

European Journal of Operational Research, nº 73 (1994)

Development of new methodologies in sample treatment for proteomics workflow based on enzymatic probe sonication technology and mass spectrometry

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia, for the degree of Doctor of Philosophy in Biochemistry