Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had 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 behaviour. 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 dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Inglês: atividade de enriquecimento ou disciplina curricular?

Mestrado em Ensino Precoce do Inglês

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Relatório de estágio “Abotoa Lda./SKYPRO Shoes”

Dissertação apresentada ao Instituto Politécnico do Porto-Instituto Superior de Contabilidade e Administração do Porto, para obtenção do Grau de Mestre em Empreendedorismo e Internacionalização, sob orientação de Professor Doutor Orlando Manuel Lima Rua

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

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.

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.

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.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Planeamento estratégico através da implementação da metodologia Balanced Scorecard numa empresa de construção

Atualmente, existe uma constante necessidade de inovação tecnológica e organizacional das empresas, face às exigências do mercado e ao aumento constante da competitividade. No contexto da economia atual, e para contrariar a falta de investimento dos últimos anos, as empresas concentram-se no desenvolvimento de negócios bem estruturados e organizados, que satisfaçam as exigências de cada cliente. Revela-se, assim, muito importante a avaliação de desempenho para o alcance do seu sucesso, permitindo alinhar as atividades operacionais com a visão organizacional, fomentar a comunicação da estratégia e gerir o seu desempenho. É neste contexto que ganha importância o Balanced Scorecard, um instrumento que permite avaliar o desempenho organizacional, funcionando também como uma ferramenta de gestão. Tem, portanto, como principais objetivos definir claramente uma estratégia, definir objetivos, indicadores e medidas estratégicas, planear e estabelecer metas e melhorar continuamente os resultados obtidos. Esta ferramenta analisa a organização em quatro diferentes perspetivas: financeira, clientes, processos internos e apendizagem e crescimento. Cada uma delas deve estrututrar os seus próprios objetivos, indicadores, metas e iniciativas de forma a contribuir para o desenvolvimento da estratégia da organização e avaliar o seu desempenho. O desafio deste relatório é adaptar esta metodologia durante a realização de um estágio curricular na empresa Bysteel, que tem por base a implementação de um processo de gestão estratégica nas suas empreitadas, através do Balanced Scorecard. Este planemamento surge como um dos principais requisitos para uma gestão eficaz de custos e de tempo planeados para cada empreitada da Bysteel. Este processo torna-se bastante complexo dado que cada obra apresenta diferentes particularidades. A implementação desta metodologia numa empresa desta dimensão é um fator muito motivador, tornando-se ainda mais interessante pelo facto de se tratar de uma implementação numa nova vertente.

Melhoria de linha de produção na indústria automóvel

No âmbito da unidade curricular Dissertação do 2ºano do Mestrado em Engenharia mecânica – Ramo de Gestão Industrial do Instituto Superior de Engenharia do Porto, o presente trabalho de dissertação foi enquadrado num projeto industrial para a racionalização de uma linha de produção. O projeto foi desenvolvido numa empresa de produção de componentes por alumínio injetado e seguidamente processos de maquinação, para mercados a nível mundial destinados à indústria automóvel. A realização deste projeto teve como objetivo a melhoria de uma linha de produção na empresa TESCO Componentes para automóveis, Lda. recorrendo a métodos e ferramentas utilizadas pela Gestão Industrial tais como, Lean, 5’S, PDCA assim como ferramentas específicas de balanceamento e otimização de linhas. Os métodos e ferramentas utilizados permitiram a seleção da linha, a definição e caraterização do modelo em produção e posto de trabalho, isto com o objetivo reduzir os custos, aumentar a produtividade e conduzir a melhorias ao nível de qualidade. Através de análises realizadas aos dados de produção foi possível a avaliação do indicador de eficiência operacional do equipamento, através dos OEE’s, o que possibilitou uma análise mais compreensiva da capacidade e cadência da linha de produção. Com a aplicação dos métodos ligados à filosofia de produção em Lean, foi possível atingir-se os objetivos inicialmente definidos, e em alguns casos foi possível ultrapassá-los. Em função da abordagem integrada que foi seguida, conseguiu-se uma redução de um posto de trabalho, traduzindo-se na redução do custo de fabrico não deixando de referir que também houve melhorias a nível de qualidade do produto. Reduzindo a possibilidade de falhas com as especificações estabelecidas pelos clientes. Como efeito positivo deste projeto pode-se apontar o fato de que a empresa Tesco Componentes para Automóveis, Lda. aumentou a sua competitividade com a oportunidade de redução do preço final do produto e também com o aumento da qualidade do mesmo.

Significance of Biogenic Amines in Cold-Smoked Fish and Their Relation to Microbiological Characteristics of Products Available in Portuguese Retail Markets

Studies on microbial characterization of cold-smoked salmon and salmon trout during cold storage were performed on samples available in the Portuguese market. Samples were also classified microbiologically according to guidelines for ready-to-eat (RTE) products. Further investigations on sample variability and microbial abilities to produce tyramine and histamine were also performed. The coefficient of variation for viable counts of different groups of microorganisms of samples collected at retail market point was high in the first 2 wk of storage, mainly in the Enterobacteriaceae group and aerobic plate count (APC), suggesting that microbiological characteristics of samples were different in numbers, even within the same batch from the same producer. This variation seemed to be decreased when storage and temperature were controlled under lab conditions. The numbers of Enterobacteriaceae were influenced by storage temperature, as indicated by low microbial numbers in samples from controlled refrigeration. Lactic acid bacteria (LAB) and Enterobacteriaceae were predominant in commercial products, a significant percentage of which were tyramine and less histamine producers. These results might be influenced by (1) the technological processes in the early stages of production, (2) contamination during the smoking process, and (3) conditions and temperature fluctuations during cold storage at retail market point of sale.