Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles

The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.

Conceptualização de um evento à luz do brand entertainment e ativação de marcas

Trabalho de projeto apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

Dynamical Stability and Predictability of Football Players: The Study of One Match

The game of football demands new computational approaches to measure individual and collective performance. Understanding the phenomena involved in the game may foster the identification of strengths and weaknesses, not only of each player, but also of the whole team. The development of assertive quantitative methodologies constitutes a key element in sports training. In football, the predictability and stability inherent in the motion of a given player may be seen as one of the most important concepts to fully characterise the variability of the whole team. This paper characterises the predictability and stability levels of players during an official football match. A Fractional Calculus (FC) approach to define a player’s trajectory. By applying FC, one can benefit from newly considered modeling perspectives, such as the fractional coefficient, to estimate a player’s predictability and stability. This paper also formulates the concept of attraction domain, related to the tactical region of each player, inspired by stability theory principles. To compare the variability inherent in the player’s process variables (e.g., distance covered) and to assess his predictability and stability, entropy measures are considered. Experimental results suggest that the most predictable player is the goalkeeper while, conversely, the most unpredictable players are the midfielders. We also conclude that, despite his predictability, the goalkeeper is the most unstable player, while lateral defenders are the most stable during the match.

Protótipo Eurobolso

O projeto idealizado para a realização da presente tese de mestrado tem como finalidade o desenvolvimento de uma aplicação móvel para o sistema Android. Esta aplicação permitirá que, através da passagem do dispositivo móvel por um leitor NFC, seja possível realizar apostas no jogo Euromilhões da Santa Casa, onde os dados ficam gravados numa conta associada a cada jogador. Esta aplicação terá ainda muitas outras funcionalidades que permitirão criar chaves de jogo, gerir o cartão individual de cada jogador, consultar os prémios e outras informações do jogo. A realização deste projeto está dividida em três fases. A primeira fase consistiu na aquisição de todo o material necessário e estabelecimento da comunicação entre o dispositivo móvel e o desktop, por intermédio da tecnologia NFC. A segunda fase centrou-se no desenvolvimento da aplicação móvel e do servidor web, onde foram integradas as várias funcionalidades. Estabeleceu-se também a comunicação entre estes dois sistemas. Na terceira e última fase, foi realizada a criação da aplicação desktop, capaz de interagir por intermédio da tecnologia NFC, com a aplicação móvel, possibilitando a comunicação entre os dois sistemas.

Geocaching como ferramenta de Marketing Digital

O Geocaching é um jogo, criado pela Groundspeak, que consiste em esconder e encontrar objetos geolocalizados conhecidos como geocaches. A busca das geocaches é na realidade uma aventura que promove a vivência de novas experiências, o convívio entre utilizadores, a descoberta de novos espaços na natureza, a realização de jogos em tempo e cenário real, entre outros. Existem geocaches espalhadas por todo o mundo e milhares de utilizadores estão já registados no jogo. Além de passatempo, o Geocaching consegue ser uma ferramenta de marketing digital, quer para a própria Groundspeak, mas também para diferentes empresas/instituições por todo o mundo normalmente associadas à localização das geocaches. A Groundspeak é, naturalmente, a mais beneficiada uma vez que, praticamente sem investir em publicidade, conseguiu que o jogo tenha cada vez mais adeptos. A sua divulgação é essencialmente feita pelos próprios utilizadores, quer através da comunicação direta com um não utilizador, quer através de redes sociais, de eventos organizados, mas também através de outras empresas que desenvolveram aplicações com funcionalidades extra que permitem ao utilizador uma melhor experiência. O objetivo desta dissertação foi o de demonstrar como é que o Geocaching pode ser usado como uma ferramenta de Marketing Digital. Inicialmente, foi analisada a questão do Marketing Digital e das suas ferramentas, focando o Geocaching e a sua dimensão no mundo, explicando os diferentes tipos de caches e de que forma as mesmas podem ser utilizadas como ferramentas de marketing. Como elemento de validação, foi concebida, desenvolvida e validada uma wherigo (um tipo de geocache), que consiste num jogo virtual onde o progresso do jogador depende das tarefas realizadas e da sua movimentação geolocalizada. A wherigo criada no âmbito do projeto é um meio de marketing digital, de divulgação do Castelo de Santa Maria da Feira, realizada de uma forma interativa e divertida, através de questionários, desafios e fantasia. O jogo incita a percorrer os jardins que rodeiam o Castelo bem como o interior do mesmo e permite ainda o acesso dos jogadores ao Castelo com desconto de geocacher na aquisição do ingresso. Os objetivos propostos inicialmente foram completamente cumpridos, sendo que o jogo já se encontra disponível para ser jogado por geocachers e foi por eles avaliado muito positivamente.

Matemática 100 stress: um projeto aberto no IPP

A Era Tecnológica em que nos vemos inseridos, cujos avanços acontecem a uma velocidade vertiginosa exige, por parte das Instituições de Ensino Superior (IES) uma atitude proactiva no sentido de utilização dos muitos recursos disponíveis. Por outro lado, os elementos próprios da sociedade da informação – flexibilidade, formação ao longo da vida, acessibilidade à informação, mobilidade, entre muito outros – atuam como fortes impulsionadores externos para que as IES procurem e analisem novas modalidades formativas. Perante a mobilidade crescente, que se tem revelado massiva, a aprendizagem tende a ser cada vez mais individualizada, visual e prática. A conjugação de várias formas/tipologias de transmissão de conhecimento, de métodos didáticos e mesmo de ambientes e situações de aprendizagem induzem uma melhor adaptação do estudante, que poderá procurar aqueles que melhor vão ao encontro das suas expetativas, isto é, favorecem um processo de ensino-aprendizagem eficiente na perspetiva da forma de aprender de cada um. A definição de políticas estratégicas relacionadas com novas modalidades de ensino/formação tem sido uma preocupação constante na nossa instituição, nomeadamente no domínio do ensino à distância, seja ele e-Learning, b-Learning ou, mais recentemente, “open-Learning”, onde se inserem os MOOC – Massive Open Online Courses (não esquecendo a vertente m-Learning), de acordo com as várias tendências europeias (OECD, 2007) (Comissão Europeia, 2014) e com os objetivos da “Europa 2020”. Neste sentido surge o Projeto Matemática 100 STRESS, integrado no projeto e-IPP | Unidade de e-Learning do Politécnico do Porto que criou a sua plataforma MOOC, abrindo em junho de 2014 o seu primeiro curso – Probabilidades e Combinatória. Pretendemos dar a conhecer este Projeto, e em particular este curso, que envolveu vários docentes de diferentes unidades orgânicas do IPP.

Maximizing the Social Welfare of Virtual Power Players Operation in Case of Excessive Wind Power

The integration of growing amounts of distributed generation in power systems, namely at distribution networks level, has been fostered by energy policies in several countries around the world, including in Europe. This intensive integration of distributed, non-dispatchable, and natural sources based generation (including wind power) has caused several changes in the operation and planning of power systems and of electricity markets. Sometimes the available non-dispatchable generation is higher than the demand. This generation must be used; otherwise it is wasted if not stored or used to supply additional demand. New policies and market rules, as well as new players, are needed in order to competitively integrate all the resources. The methodology proposed in this paper aims at the maximization of the social welfare in a distribution network operated by a virtual power player that aggregates and manages the available energy resources. When facing a situation of excessive non-dispatchable generation, including wind power, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. This method is especially useful when actual and day-ahead resources forecast differ significantly. The distribution network characteristics and concerns are addressed by including the network constraints in the optimization model. The proposed methodology has been implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20.310 consumers and 548 distributed generators, some of them non-dispatchable and with must take contracts. The implemented scenario corresponds to a real day in Portuguese power system.

SCADA House Intelligent Management for Energy Efficiency Analysis in Domestic Consumers

The implementation of smart homes allows the domestic consumer to be an active player in the context of the Smart Grid (SG). This paper presents an intelligent house management system that is being developed by the authors to manage, in real time, the power consumption, the micro generation system, the charge and discharge of the electric or plug-in hybrid vehicles, and the participation in Demand Response (DR) programs. The paper proposes a method for the energy efficiency analysis of a domestic consumer using the SCADA House Intelligent Management (SHIM) system. The main goal of the present paper is to demonstrate the economic benefits of the implemented method. The case study considers the consumption data of some real cases of Portuguese house consumption over 30 days of June of 2012, the Portuguese real energy price, the implementation of the power limits at different times of the day and the economic benefits analysis.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Intelligent Remuneration and Tariffs for Virtual Power Players

Power systems have been through deep changes in recent years, namely due to the operation of competitive electricity markets in the scope the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities that have emerged. Virtual Power Players (VPPs) are a new type of player that allows aggregating a diversity of players (Distributed Generation (DG), Storage Agents (SA), Electrical Vehicles (V2G) and consumers) to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players’ benefits. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. This paper proposes a model to implement fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in the scope of electricity markets.

Integration in MASCEM of the Joint Dispatch of Energy and Reserves Provided by Generation and Demand Resources

The provision of reserves in power systems is of great importance in what concerns keeping an adequate and acceptable level of security and reliability. This need for reserves and the way they are defined and dispatched gain increasing importance in the present and future context of smart grids and electricity markets due to their inherent competitive environment. This paper concerns a methodology proposed by the authors, which aims to jointly and optimally dispatch both generation and demand response resources to provide the amounts of reserve required for the system operation. Virtual Power Players are especially important for the aggregation of small size demand response and generation resources. The proposed methodology has been implemented in MASCEM, a multi agent system also developed at the authors’ research center for the simulation of electricity markets.

Reactive power management strategies in future smart grids

The reactive power management in distribution network with large penetration of distributed energy resources is an important task in future power systems. The control of reactive power allows the inclusion of more distributed recourses and a more efficient operation of distributed network. Currently, the reactive power is only controlled in large power plants and in high and very high voltage substations. In this paper, several reactive power control strategies considering a smart grids paradigm are proposed. In this context, the management of distributed energy resources and of the distribution network by an aggregator, namely Virtual Power Player (VPP), is proposed and implemented in a MAS simulation tool. The proposed methods have been computationally implemented and tested using a 32-bus distribution network with intensive use of distributed resources, mainly the distributed generation based on renewable resources. Results concerning the evaluation of the reactive power management algorithms are also presented and compared.

Multi-Agent Based Smart Grid Management and Simulation: Situation Awareness and Learning in a Test Bed with Simulated and Real Installations and Players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.

Adaptive Learning in Games: Defining Profiles of Competitor Players

Artificial Intelligence has been applied to dynamic games for many years. The ultimate goal is creating responses in virtual entities that display human-like reasoning in the definition of their behaviors. However, virtual entities that can be mistaken for real persons are yet very far from being fully achieved. This paper presents an adaptive learning based methodology for the definition of players’ profiles, with the purpose of supporting decisions of virtual entities. The proposed methodology is based on reinforcement learning algorithms, which are responsible for choosing, along the time, with the gathering of experience, the most appropriate from a set of different learning approaches. These learning approaches have very distinct natures, from mathematical to artificial intelligence and data analysis methodologies, so that the methodology is prepared for very distinct situations. This way it is equipped with a variety of tools that individually can be useful for each encountered situation. The proposed methodology is tested firstly on two simpler computer versus human player games: the rock-paper-scissors game, and a penalty-shootout simulation. Finally, the methodology is applied to the definition of action profiles of electricity market players; players that compete in a dynamic game-wise environment, in which the main goal is the achievement of the highest possible profits in the market.