MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.

Virtual power producers simulation - negotiating renewable distributed generation in competitive electricity markets

Sustainable development concerns made renewable energy sources to be increasingly used for electricity distributed generation. However, this is mainly due to incentives or mandatory targets determined by energy policies as in European Union. Assuring a sustainable future requires distributed generation to be able to participate in competitive electricity markets. To get more negotiation power in the market and to get advantages of scale economy, distributed generators can be aggregated giving place to a new concept: the Virtual Power Producer (VPP). VPPs are multi-technology and multisite heterogeneous entities that should adopt organization and management methodologies so that they can make distributed generation a really profitable activity, able to participate in the market. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, in the context of MASCEM, a multi-agent based eletricity market simulator.

Dgenco market balancing - cost repartition under light regulation markets

In this paper we present a new methodology, based in game theory, to obtain the market balancing between Distribution Generation Companies (DGENCO), in liberalized electricity markets. The new contribution of this methodology is the verification of the participation rate of each agent based in Nucléolo Balancing and in Shapley Value. To validate the results we use the Zaragoza Distribution Network with 42 Bus and 5 DGENCO.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

Gestão de projectos de tradução e de localização - do conceito ao modelo

O crescimento do mercado electrónico e o aumento das comunicações além-fronteiras, resultante sobretudo da difusão da Internet, repercutiu-se, de modo intenso, na indústria de tradução, particularmente, na exigência crescente da tradução de websites, e, sobretudo, na localização de software. Para melhor perceber esta realidade e, de modo a contribuir para uma maior sistematização do conhecimento nesta área, o presente artigo inicia-se com uma breve análise sobre a evolução dos conceitos e dos mercados da tradução e da localização. Procura distinguir entre os diferentes prestadores de serviços de tradução, nomeadamente, empresas e agências e descreve o processo seguido na elaboração de projectos de localização, reconhecendo a tradução como elemento integrante do projecto de localização. Com base na interpretação dos conceitos de projecto e de gestão de projectos descreve-se o processo de gestão de projectos de tradução. Sendo o gestor de projectos o elemento essencial deste processo, analisam-se as funções e competências do mesmo e descrevem-se as tarefas e processos usados por este profissional, tendo em conta o ciclo de vida e os elementos que compõem o processo de gestão de projectos. A partir desta análise propõe-se um modelo de gestão de projectos de tradução/localização baseado na conjugação dos diferentes elementos afectos à gestão e ao gestor de projectos e que procura sintetizar todo o processo inerente à gestão de projectos de tradução/localização. O modelo resulta de dois pontos de vista que se interligam: o da análise e reflexão sobre o estado-da-arte e o da análise empírica dos dados recolhidos no dia-a-dia no universo de trabalho de uma empresa de tradução. Com este modelo, que retrata um processo cíclico e dinâmico, pretende-se, por um lado, ilustrar a complexidade do processo da gestão de projectos e demonstrar a importância das funções do gestor de projectos no vasto universo que é o da tradução e, por outro, desenvolver e propor um modelo de gestão de projectos aplicável a empresas de tradução e de localização.

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.

Software transactional memory as a building block for parallel embedded realtime systems

The recent trends of chip architectures with higher number of heterogeneous cores, and non-uniform memory/non-coherent caches, brings renewed attention to the use of Software Transactional Memory (STM) as a fundamental building block for developing parallel applications. Nevertheless, although STM promises to ease concurrent and parallel software development, it relies on the possibility of aborting conflicting transactions to maintain data consistency, which impacts on the responsiveness and timing guarantees required by embedded real-time systems. In these systems, contention delays must be (efficiently) limited so that the response times of tasks executing transactions are upper-bounded and task sets can be feasibly scheduled. In this paper we assess the use of STM in the development of embedded real-time software, defending that the amount of contention can be reduced if read-only transactions access recent consistent data snapshots, progressing in a wait-free manner. We show how the required number of versions of a shared object can be calculated for a set of tasks. We also outline an algorithm to manage conflicts between update transactions that prevents starvation.

Addressing software impact in the design of remote laboratories

Remote Laboratories or WebLabs constitute a first-order didactic resource in engineering faculties. However, in many cases, they lack a proper software design, both in the client and server side, which degrades their quality and academic usefulness. This paper presents the main characteristics of a Remote Laboratory, analyzes the software technologies to implement the client and server sides in a WebLab, and correlates these technologies with the characteristics to facilitate the selection of a technology to implement a WebLab. The results obtained suggest the adoption of a Service Oriented Laboratory Architecture-based approach for the design of future Remote Laboratories so that client-agnostic Remote Laboratories and Remote Laboratory composition are enabled. The experience with the real Remote Laboratory, WebLab-Deusto, is also presented.

Marcação CE de dispositivos médicos e segurança em software

Com o crescente aumento da Teleradiologia, sentiu-se necessidade de criar mais e melhores softwares para sustentar esse crescimento. O presente trabalho pretende abordar a temática da certificação de software e a sua marcação CE, pois para dar entrada no mercado Europeu todos os Dispositivos Médicos (DM) têm de estar devidamente certificados. Para efetuar a marcação CE e a certificação serão estudadas normas e normativos adequados para marcação de DM ao nível Europeu e também dos Estados Unidos da América. A temática da segurança de dados pessoais será também estudada de forma a assegurar que o dispositivo respeite a legislação em vigor. Este estudo tem como finalidade a certificação de um software proprietário da efficientia sysPACS, um serviço online abrangente, que permite a gestão integrada do armazenamento e distribuição de imagens médicas para apoio ao diagnóstico.

Implementação de um software de gestão de logística no Hospital Agostinho Ribeiro

O presente relatório possui o propósito de apresentar o estágio curricular desenvolvido na empresa “Santa Casa da Misericórdia de Felgueiras”, iniciado a 1 de maio de 2013, englobando horas de aprendizagem e consolidação de conhecimentos adquiridos no percurso académico. A realização do presente estágio está enquadrada no protocolo celebrado entre a “Santa Casa da Misericórdia de Felgueiras” e a Associação de Politécnicos do Norte, cumprindo desta forma o art.º 2 e 3 do Regulamento de Estágios Curriculares da Associação de Politécnicos do Norte (APNOR). A realização do estágio, bem como a realização deste relatório, é o produto final de meses de experiência laboral, pesquisa bibliográfica e de reflexão, de um ano curricular. Com a realização deste estágio e do relatório permitiu-me concluir acerca de como é elaborada a gestão hospitalar e a sua importância no contexto empresarial. A gestão contribui com o fornecimento de informações económicas nos diferentes usuários como: Gestores, Governo, Sindicato, Funcionários.

A Facilidade de aprendizagem de um software como medida da sua aceitabilidade

ISCAP 9 e 10 de Maio de 1997

Practical software improve design and performance of a randomly packed air stripping column for removing volatile organic compounds from water

STRIPPING is a software application developed for the automatic design of a randomly packing column where the transfer of volatile organic compounds (VOCs) from water to air can be performed and to simulate it’s behaviour in a steady-state. This software completely purges any need of experimental work for the selection of diameter of the column, and allows a choice, a priori, of the most convenient hydraulic regime for this type of operation. It also allows the operator to choose the model used for the calculation of some parameters, namely between the Eckert/Robbins model and the Billet model for estimating the pressure drop of the gaseous phase, and between the Billet and Onda/Djebbar’s models for the mass transfer. Illustrations of the graphical interface offered are presented.

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.

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.