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.

Demonstration of the Multi-Agent Simulator of Competitive Electricity Markets

Electricity markets are complex environments with very particular characteristics. A critical issue concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, performed so that the competitiveness could be increased, but with exponential implications in the increase of the complexity and unpredictability in those markets’ scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behavior. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This paper presents the Multi-Agent System for Competitive Electricity Markets (MASCEM) – a simulator based on multi-agent technology that provides a realistic platform to simulate electricity markets, the numerous negotiation opportunities and the participating entities.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Real-Time Simulation of energy Management in a Domestic Consumer

Recent and future changes in power systems, mainly in the smart grid operation context, are related to a high complexity of power networks operation. This leads to more complex communications and to higher network elements monitoring and control levels, both from network’s and consumers’ standpoint. The present work focuses on a real scenario of the LASIE laboratory, located at the Polytechnic of Porto. Laboratory systems are managed by the SCADA House Intelligent Management (SHIM), already developed by the authors based on a SCADA system. The SHIM capacities have been recently improved by including real-time simulation from Opal RT. This makes possible the integration of Matlab®/Simulink® real-time simulation models. The main goal of the present paper is to compare the advantages of the resulting improved system, while managing the energy consumption of a domestic consumer.

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.

Towards a unified European electricity market: The contribution of data-mining to support realistic simulation studies

Worldwide electricity markets have been evolving into regional and even continental scales. The aim at an efficient use of renewable based generation in places where it exceeds the local needs is one of the main reasons. A reference case of this evolution is the European Electricity Market, where countries are connected, and several regional markets were created, each one grouping several countries, and supporting transactions of huge amounts of electrical energy. The continuous transformations electricity markets have been experiencing over the years create the need to use simulation platforms to support operators, regulators, and involved players for understanding and dealing with this complex environment. This paper focuses on demonstrating the advantage that real electricity markets data has for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations will bring to the participant countries. A case study using MASCEM (Multi-Agent System for Competitive Electricity Markets) is presented, with a scenario based on real data, simulating the European Electricity Market environment, and comparing its performance when using several different market mechanisms.

Dynamic Approach and Testbed for Small and Medium Players Simulation in Smart Grid Environments

The Smart Grid environment allows the integration of resources of small and medium players through the use of Demand Response programs. Despite the clear advantages for the grid, the integration of consumers must be carefully done. This paper proposes a system which simulates small and medium players. The system is essential to produce tests and studies about the active participation of small and medium players in the Smart Grid environment. When comparing to similar systems, the advantages comprise the capability to deal with three types of loads – virtual, contextual and real. It can have several loads optimization modules and it can run in real time. The use of modules and the dynamic configuration of the player results in a system which can represent different players in an easy and independent way. This paper describes the system and all its capabilities.

Multi-agent Simulation of Bilateral Contracting in Competitive Electricity Markets

Traditional vertically integrated power utilities around the world have evolved from monopoly structures to open markets that promote competition among suppliers and provide consumers with a choice of services. Market forces drive the price of electricity and reduce the net cost through increased competition. Electricity can be traded in both organized markets or using forward bilateral contracts. This article focuses on bilateral contracts and describes some important features of an agent-based system for bilateral trading in competitive markets. Special attention is devoted to the negotiation process, demand response in bilateral contracting, and risk management. The article also presents a case study on forward bilateral contracting: a retailer agent and a customer agent negotiate a 24h-rate tariff.

Realistic Multi-Agent Simulation of Competitive Electricity Markets

The dynamism and ongoing changes that the electricity markets sector is constantly suffering, enhanced by the huge increase in competitiveness, create the need of using simulation platforms to support operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents an enhanced electricity market simulator, based on multi-agent technology, which provides an advanced simulation framework for the study of real electricity markets operation, and the interactions between the involved players. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) uses real data for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations bring to different countries. Also, the development of an upper-ontology to support the communication between participating agents, provides the means for the integration of this simulator with other frameworks, such as MAN-REM (Multi-Agent Negotiation and Risk Management in Electricity Markets). A case study using the enhanced simulation platform that results from the integration of several systems and different tools is presented, with a scenario based on real data, simulating the MIBEL electricity market environment, and comparing the simulation performance with the real electricity market results.

Real-Time Simulation of Real-Time Pricing Demand Response to Meet Wind Variations

Recent changes of paradigm in power systems opened the opportunity to the active participation of new players. The small and medium players gain new opportunities while participating in demand response programs. This paper explores the optimal resources scheduling in two distinct levels. First, the network operator facing large wind power variations makes use of real time pricing to induce consumers to meet wind power variations. Then, at the consumer level, each load is managed according to the consumer preferences. The two-level resources schedule has been implemented in a real-time simulation platform, which uses hardware for consumer’ loads control. The illustrative example includes a situation of large lack of wind power and focuses on a consumer with 18 loads.

Distributed Intelligent Management of Microgrids using a Multi-Agent Simulation Platform

Multi-agent approaches have been widely used to model complex systems of distributed nature with a large amount of interactions between the involved entities. Power systems are a reference case, mainly due to the increasing use of distributed energy sources, largely based on renewable sources, which have potentiated huge changes in the power systems’ sector. Dealing with such a large scale integration of intermittent generation sources led to the emergence of several new players, as well as the development of new paradigms, such as the microgrid concept, and the evolution of demand response programs, which potentiate the active participation of consumers. This paper presents a multi-agent based simulation platform which models a microgrid environment, considering several different types of simulated players. These players interact with real physical installations, creating a realistic simulation environment with results that can be observed directly in the reality. A case study is presented considering players’ responses to a demand response event, resulting in an intelligent increase of consumption in order to face the wind generation surplus.

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.

Modelação e identificação de motor DC

O modelo matemático de um sistema real permite o conhecimento do seu comportamento dinâmico e é geralmente utilizado em problemas de engenharia. Por vezes os parâmetros utilizados pelo modelo são desconhecidos ou imprecisos. O envelhecimento e o desgaste do material são fatores a ter em conta pois podem causar alterações no comportamento do sistema real, podendo ser necessário efetuar uma nova estimação dos seus parâmetros. Para resolver este problema é utilizado o software desenvolvido pela empresa MathWorks, nomeadamente, o Matlab e o Simulink, em conjunto com a plataforma Arduíno cujo Hardware é open-source. A partir de dados obtidos do sistema real será aplicado um Ajuste de curvas (Curve Fitting) pelo Método dos Mínimos Quadrados de forma a aproximar o modelo simulado ao modelo do sistema real. O sistema desenvolvido permite a obtenção de novos valores dos parâmetros, de uma forma simples e eficaz, com vista a uma melhor aproximação do sistema real em estudo. A solução encontrada é validada com recurso a diferentes sinais de entrada aplicados ao sistema e os seus resultados comparados com os resultados do novo modelo obtido. O desempenho da solução encontrada é avaliado através do método das somas quadráticas dos erros entre resultados obtidos através de simulação e resultados obtidos experimentalmente do sistema real.

Avaliação do Desempenho Energético e da Qualidade do Ar Interior do Edifício Principal do Parque Biológico de Vila Nova de Gaia

A presente dissertação tem como principal propósito avaliar o desempenho energético e a qualidade do ar interior do edifício principal do Parque Biológico de Vila Nova de Gaia (PBG). Para esse efeito, este estudo relaciona os termos definidos na legislação nacional em vigor até à presente data, e referentes a esta área de atuação, em particular, os presentes no SCE, RSECE, RCCTE e RSECE-QAI. Para avaliar o desempenho energético, procedeu-se numa primeira fase ao processo de auditoria no local e posteriormente à realização de uma simulação dinâmica detalhada, cuja modelação do edifício foi feita com recurso ao software DesignBuilder. Após a validação do modelo simulado, por verificação do desvio entre os consumos energéticos registados nas faturas e os calculados na simulação, igual a 5,97%, foi possível efetuar a desagregação dos consumos em percentagem pelos diferentes tipos de utilizações. Foi também possível determinar os IEE real e nominal, correspondendo a 29,9 e 41.3 kgep/m2.ano, respetivamente, constatando-se através dos mesmos que o edifício ficaria dispensado de implementar um plano de racionalização energética (PRE) e que a classe energética a atribuir é a C. Contudo, foram apresentadas algumas medidas de poupança de energia, de modo a melhorar a eficiência energética do edifício e reduzir a fatura associada. Destas destacam-se duas propostas, a primeira propõe a alteração do sistema de iluminação interior e exterior do edifício, conduzindo a uma redução no consumo de eletricidade de 47,5 MWh/ano, com um período de retorno de investimento de 3,5 anos. A segunda está relacionada com a alteração do sistema de produção de água quente para o aquecimento central, através do incremento de uma caldeira a lenha ao sistema atual, que prevê uma redução de 50 MWh no consumo de gás natural e um período de retorno de investimento de cerca de 4 anos. Na análise realizada à qualidade do ar interior (QAI), os parâmetros quantificados foram os exigidos legalmente, excetuando os microbiológicos. Deste modo, para os parâmetros físicos, temperatura e humidade relativa, obtiveram-se os resultados médios de 19,7ºC e 66,9%, respetivamente, ligeiramente abaixo do previsto na legislação (20,0ºC no período em que foi feita a medição, inverno). No que diz respeito aos parâmetros químicos, os valores médios registados para as concentrações de dióxido de carbono (CO2), monóxido de carbono (CO), ozono (O3), formaldeído (HCHO), partículas em suspensão (PM10) e radão, foram iguais a 580 ppm, 0,2 ppm, 0,06 ppm, 0,01 ppm, 0,07 mg/m3 e 196 Bq/m3, respetivamente, verificando-se que estão abaixo dos valores máximos de referência presentes no regulamento (984 ppm, 10,7 ppm, 0,10 ppm, 0,08 ppm, 0,15 mg/m3 e 400 Bq/m3). No entanto, o parâmetro relativo aos compostos orgânicos voláteis (COV) teve um valor médio igual a 0,84 ppm, bastante acima do valor máximo de referência (0,26 ppm). Neste caso, terá que ser realizada uma nova série de medições utilizando meios cromatográficos, para avaliar qual(ais) são o(s) agente(s) poluidor(es), de modo a eliminar ou atenuar as fontes de emissão.