A simulation tool for demand response programs implementation

The design and development of simulation models and tools for Demand Response (DR) programs are becoming more and more important for adequately taking the maximum advantages of DR programs use. Moreover, a more active consumers’ participation in DR programs can help improving the system reliability and decrease or defer the required investments. DemSi, a DR simulator, designed and implemented by the authors of this paper, allows studying DR actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. DemSi considers the players involved in DR actions, and the results can be analyzed from each specific player point of view.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Virtual power producers market strategies

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper detail some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study.

Complex market integration in MASCEM electricity market simulator

The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players’ interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Ancillary service market simulation

Power systems operation in a liberalized environment requires that market players have access to adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper deals with ancillary services negotiation in electricity markets. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of ancillary services using two different methods (Linear Programming and Genetic Algorithm approaches) is included in the paper.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tool must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Ancillary services dispatch using linear programming and genetic algorithm approaches

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Self-organization for scheduling in agile manufacturing

Agility refers to the manufacturing system ability to rapidly adapt to market and environmental changes in efficient and cost-effective ways. This paper addresses the development of self-organization methods to enhance the operations of a scheduling system, by integrating scheduling system, configuration and optimization into a single autonomic process requiring minimal manual intervention to increase productivity and effectiveness while minimizing complexity for users. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to build future Decision Support Systems (DSS) for Scheduling in agile manufacturing environments.

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.

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 he 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.

Dimensionamento de uma central de miniprodução fotovoltaica para uma exploração agrícola direcionada à indústria de laticínios

Este documento apresenta o trabalho desenvolvido no âmbito da disciplina de “Dissertação/Projeto/Estágio”, do 2º ano do Mestrado em Energias Sustentáveis. O crescente consumo energético das sociedades desenvolvidas e emergentes, associado ao consequente aumento dos custos de energia e dos danos ambientais resultantes, promove o desenvolvimento de novas formas de produção de energia, as quais têm como prioridade a sua obtenção ao menor custo possível e com reduzidos impactos ambientais. De modo a poupar os recursos naturais e reduzir a emissão com gases de efeito de estufa, é necessária a diminuição do consumo de energia produzida a partir de combustíveis fósseis. Assim, devem ser criadas alternativas para um futuro sustentável, onde as fontes renováveis de energia assumam um papel fundamental. Neste sentido, a produção de energia elétrica, através de sistemas solares fotovoltaicos, surge como uma das soluções. A presente dissertação tem como principal objetivo a realização do dimensionamento de uma central de miniprodução fotovoltaica, com ligação à rede elétrica, em uma exploração agrícola direcionada à indústria de laticínios, e o seu respetivo estudo de viabilidade económica. A exploração agrícola, que serve de objeto de estudo, está localizada na Ilha Graciosa, Açores, sendo a potência máxima a injetar na Rede Elétrica de Serviço Público, pela central de miniprodução, de 10 kW. Para o dimensionamento foi utilizado um software apropriado e reconhecido na área da produção de energia elétrica através de sistemas fotovoltaicos – o PVsyst –, compreendendo as seguintes etapas: a) definição das caraterísticas do local e do projeto; b) seleção dos módulos fotovoltaicos; c) seleção do inversor; d) definição da potência de ligação à rede elétrica da unidade de miniprodução. Posteriormente, foram estudadas diferentes hipóteses de sistemas fotovoltaicos, que se distinguem na opção de estrutura de fixação utilizada: dois sistemas fixos e dois com eixo incorporado. No estudo de viabilidade económica foram realizadas duas análises distintas a cada um dos sistemas fotovoltaicos considerados no dimensionamento, nomeadamente: uma análise em regime remuneratório bonificado e uma análise em regime remuneratório geral. Os resultados obtidos nos indicadores económicos do estudo de viabilidade económica realizado, serviram de apoio à decisão pelo sistema fotovoltaico mais favorável ao investimento. Conclui-se que o sistema fotovoltaico com inclinação adicional é a opção mais vantajosa em ambos os regimes remuneratórios analisados. Comprova-se, assim, que o sistema fotovoltaico com maior valor de produção de energia elétrica anual, que corresponde ao sistema fotovoltaico de dois eixos, não é a opção com maior rentabilidade em termos económicos, isto porque a remuneração proveniente da sua produção excedente não é suficiente para colmatar o valor do investimento mais acentuado de modo a obter indicadores económicos mais favoráveis, que os do sistema fotovoltaico com inclinação adicional. De acordo com o estudo de viabilidade económica efetuado independentemente do sistema fotovoltaico que seja adotado, é recuperado o investimento realizado, sendo a remuneração efetiva superior à que foi exigida. Assim, mesmo tendo em consideração o risco associado, comprova-se que todos os sistemas fotovoltaicos, em qualquer dos regimes remuneratórios, correspondem a investimentos rentáveis.

Avaliação de realce tardio do miocárdio por tomografia computorizada

A distinção entre miocárdio atordoado e danificado tem sido uma preocupação relevante, no cenário de um enfarte agudo do miocárdio (EAM). A avaliação da viabilidade do miocárdio, pós-enfarte, é de importância vital, no contexto clínico, principalmente numa fase inicial. Actualmente a Ressonância Magnética Cardíaca é o exame de referência para a avaliação de viabilidade do miocárdio. No entanto, é um exame com elevado custo e de difícil acesso. Estudos preliminares demonstraram potencial na utilização de imagens por Tomografia Computorizada para avaliação da área de enfarte, quer em estudos animais quer em humanos. É objectivo desta tese verificar a utilidade de um protocolo de avaliação de viabilidade do miocárdio, com base em imagens de realce tardio (RT) por Tomografia Computorizada, após um procedimento de intervenção coronária percutânea, no contexto de enfarte agudo do miocárdio com elevação do segmento ST (STEMI). Pretende-se igualmente contribuir para a análise da imagem médica do miocárdio, proporcionando métodos de quantificação do RT e software de suporte à decisão médica nesta modalidade de imagem substancialmente recente. São avaliados vários processos para a quantificação do volume de RT, incluindo um método inovador baseado na detecção automática do miocárdio normal. _E ainda proposto um algoritmo para detecção automática do grau de transmuralidade, por segmento do miocárdio, e comparado o seu grau de eficiência face ao diagnóstico médico dos mesmos exames. Apesar do reduzido número de exames utilizado para validação das técnicas descritas nesta tese, os resultados são bastante promissores e podem constituir uma mais-valia no auxilio à gestão do paciente com EAM.

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.