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.

A long-term swarm intelligence hedging tool applied to electricity markets

This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.

Electricity markets: transmission prices methods

In the context of electricity markets, transmission pricing is an important tool to achieve an efficient operation of the electricity system. The electricity market is influenced by several factors; however the transmission network management is one of the most important aspects, because the network is a natural monopoly. The transmission tariffs can help to regulate the market, for this reason transmission tariffs must follow strict criteria. This paper presents the following methods to tariff the use of transmission networks by electricity market players: Post-Stamp Method; MW-Mile Method Distribution Factors Methods; Tracing Methodology; Bialek’s Tracing Method and Locational Marginal Price. A nine bus transmission network is used to illustrate the application of the tariff methods.

Auction of financial transmission rights in electricity market environment

An auction model is used to increase the individual profits for market players with products they do not use. A Financial Transmission Rights Auction has the goal of trade transmission rights between Bidders and helps them raise their own profits. The ISO plays a major rule on keep the system in technical limits without interfere on the auctions offers. In some auction models the ISO decide want bids are implemented on the network, always with the objective maximize the individual profits for all bidders in the auction. This paper proposes a methodology for a Financial Transmission Rights Auction and an informatics application. The application receives offers from the purchase and sale side and considers bilateral contracts as Base Case. This goal is maximize the individual profits within the system in their technical limits. The paper includes a case study for the 30 bus IEEE test case.

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.

An integrated system to support electricity tariff contract definition

This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.

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.

Avaliação energética de uma escola

Mestrado em Engenharia Química

Produção de hidrogénio por fotoeletrólise da água – aproveitamento do subproduto oxigénio

A noção de Economia relativa ao Hidrogénio no vocabulário dos líderes políticos e empresariais tem vindo a mudar sobretudo pela preocupação da poluição global, segurança energética e mudanças climáticas, para além do crescente domínio técnico dos cientistas e engenheiros. O interesse neste composto, que é o elemento mais simples e abundante no universo, está a crescer, devido aos avanços tecnológicos das células de combustível – as potenciais sucessoras das baterias dos aparelhos portáteis eletrónicos, centrais elétricas e motores de combustão interna. Existem métodos já bem desenvolvidos para produzir o hidrogénio. Contudo, destacase a eletrólise da água, não só por ser um método simples mas porque pode utilizar recursos energéticos renováveis, tais como, o vento ou os painéis fotovoltaicos, e aumentar a sua eficiência. Os desafios para melhorar a utilização deste método consistem em reduzir o consumo, a manutenção e os custos energéticos e aumentar a confiança, a durabilidade e a segurança. Mais ainda, consistem em rentabilizar o subproduto oxigénio pois é um gás industrial e medicinal muito importante. Neste trabalho, estudou-se a viabilidade económica da instalação de uma unidade de produção de hidrogénio e oxigénio puros por eletrólise da água, utilizando como fonte energética a energia solar, na empresa Gasoxmed – Gases Medicinais S.A., pretendendo num futuro próximo, comercializar o hidrogénio como fonte de energia, e por outro lado, aproveitar o subproduto oxigénio para utilização industrial. Projetou-se assim uma unidade utilizando um eletrolisador da marca Proton, modelo C30, com capacidade de produção gasosa de 3 kg/h (30 m3/h) de hidrogénio e 20 kg/h (15 m3/h) de oxigénio. Os gases produzidos são comprimidos num compressor da marca RIX a 200 bares para posterior armazenamento em cilindros pressurizados. Dimensionou-se ainda um sistema de miniprodução fotovoltaico com potência 250 kW para alimentar eletricamente a instalação. A realização do projeto na nova área de produção necessitará de 1.713.963€, os quais serão adquiridos por empréstimo bancário. Definiram-se todos os custos fixos associados ao projeto que perfazem um total de 62.554€/mês para os primeiros 5 anos (duração do crédito bancário) findo o qual diminuirão para 21.204€/mês. Da comercialização do hidrogénio, do oxigénio industrial e da eletricidade produzida no sistema de miniprodução de 250 kW, prevê-se um lucro mensal de 117.925€, perfazendo assim um total líquido mensal positivo de 55.371€ durante os primeiros 5 anos e a partir daí de 96.721€/mês, resultando uma amortização do investimento inicial no final do 3º ano.

Dimensionamento de um secador em leito fluidizado para secagem de cereais

O objectivo desta tese é dimensionar um secador em leito fluidizado para secagem de cereais, nomeadamente, secagem de sementes de trigo. Inicialmente determinaram-se as condições de hidrodinâmica (velocidade de fluidização, TDH, condições mínimas de “slugging”, expansão do leito, dimensionamento do distribuidor e queda de pressão). Com as condições de hidrodinâmica definidas, foi possível estimar as dimensões físicas do secador. Neste ponto, foram realizados estudos relativamente à cinética da secagem e à própria secagem. Foi também estudado o transporte pneumático das sementes. Deste modo, determinaram-se as velocidades necessárias ao transporte pneumático e respectivas quedas de pressão. Por fim, foi realizada uma análise custos para que se soubesse o custo deste sistema de secagem. O estudo da secagem foi feito para uma temperatura de operação de 50ºC, tendo a ressalva que no limite se poderia trabalhar com 60ºC. A velocidade de operação é de 2,43 m/s, a altura do leito fixo é de 0,4 m, a qual sofre uma expansão durante a fluidização, assumindo o valor de 0,79 m. O valor do TDH obtido foi de 1,97 m, que somado à expansão do leito permite obter uma altura total da coluna de 2,76 m. A altura do leito fixo permite retirar o valor do diâmetro que é de 0,52 m. Verifica-se que a altura do leito expandido é inferior à altura mínima de “slugging” (1,20 m), no entanto, a velocidade de operação é superior à velocidade mínima de “slugging” (1,13 m/s). Como só uma das condições mínimas é cumprida, existe a possibilidade da ocorrência de “slugging”. Finalmente, foi necessário dimensionar o distribuidor, que com o diâmetro de orifício de 3 mm, valor inferior ao da partícula (3,48 mm), permite a distruibuição do fluido de secagem na coluna através dos seus 3061 orifícios. O inicio do estudo da secagem centrou-se na determinação do tempo de secagem. Além das duas temperaturas atrás referidas, foram igualmente consideradas duas humidades iniciais para os cereais (21,33% e 18,91%). Temperaturas superiores traduzem-se em tempos de secagem inferiores, paralelamente, teores de humidade inicial inferiores indicam tempos menores. Para a temperatura de 50ºC, os tempos de secagem assumiram os valores de 2,8 horas para a 21,33% de humidade e 2,7 horas para 18,91% de humidade. Foram também tidas em conta três alturas do ano para a captação do ar de secagem, Verão e Inverno representando os extremos, e a Meia- Estação. Para estes três casos, foi possível verificar que a humidade específica do ar não apresenta alterações significativas entre a entrada no secador e a corrente de saída do mesmo equipamento, do mesmo modo que a temperatura de saída pouco difere da de entrada. Este desvio de cerca de 1% para as humidades e para as temperaturas é explicado pela ausência de humidade externa nas sementes e na pouca quantidade de humidade interna. Desta forma, estes desvios de 1% permitem a utilização de uma razão de reciclagem na ordem dos 100% sem que o comportamento da secagem se altere significativamente. O uso de 100% de reciclagem permite uma poupança energética de cerca de 98% no Inverno e na Meia-Estação e de cerca de 93% no Verão. Caso não fosse realizada reciclagem, seria necessário fornecer à corrente de ar cerca de 18,81 kW para elevar a sua temperatura de 20ºC para 50ºC (Meia-Estação), cerca de 24,67 kW para elevar a sua temperatura de 10ºC para 50ºC (Inverno) e na ordem dos 8,90 kW para elevar a sua temperatura dos 35ºC para 50ºC (Verão). No caso do transporte pneumático, existem duas linhas, uma horizontal e uma vertical, logo foi necessário estimar o valor da velocidade das partículas para estes dois casos. Na linha vertical, a velocidade da partícula é cerca de 25,03 m/s e cerca de 35,95 m/s na linha horizontal. O menor valor para a linha vertical prende-se com o facto de nesta zona ter que se vencer a força gravítica. Em ambos os circuitos a velocidade do fluido é cerca de 47,17 m/s. No interior da coluna, a velocidade do fluido tem o valor de 10,90 m/s e a velocidade das partículas é de 1,04 m/s. A queda de pressão total no sistema é cerca de 2408 Pa. A análise de custos ao sistema de secagem indicou que este sistema irá acarretar um custo total (fabrico mais transporte) de cerca de 153035€. Este sistema necessita de electricidade para funcionar, e esta irá acarretar um custo anual de cerca de 7951,4€. Embora este sistema de secagem apresente a possibilidade de se realizar uma razão de reciclagem na ordem dos 100% e também seja possível adaptar o mesmo para diferentes tipos de cereais, e até outros tipos de materiais, desde que possam ser fluidizados, o seu custo impede que a realização deste investimento não seja atractiva, especialmente tendo em consideração que se trata de uma instalação à escala piloto com uma capacidade de 45 kgs.

Software de apoio à implementação de sistemas híbridos de produção de energia eléctrica baseados em energias renováveis

O aumento da população Mundial, particularmente em Países emergentes como é o caso da China e da Índia, tem-se relevado um problema adicional no que confere às dificuldades associadas ao consumo mundial de energia, pois esta situação limita inequivocamente o acesso destes milhões de pessoas à energia eléctrica para os bens básicos de sobrevivência. Uma das muitas formas de se extinguir esta necessidade, começa a ser desenvolvida recorrendo ao uso de recursos renováveis como fontes de energia. Independentemente do local do mundo onde nos encontremos, essas fontes de energia são abundantes, inesgotáveis e gratuitas. O problema reside na forma como esses recursos renováveis são geridos em função das solicitações de carga que as instalações necessitam. Sistemas híbridos podem ser usados para produzir energia em qualquer parte do mundo. Historicamente este tipo de sistemas eram aplicados em locais isolados, mas nos dias que correm podem ser usados directamente conectados à rede, permitindo que se realize a venda de energia. Foi neste contexto que esta tese foi desenvolvida, com o objectivo de disponibilizar uma ferramenta informática capaz de calcular a rentabilidade de um sistema híbrido ligado à rede ou isolado. Contudo, a complexidade deste problema é muito elevada, pois existe uma extensa panóplia de características e distintos equipamentos que se pode adoptar. Assim, a aplicação informática desenvolvida teve de ser limitada e restringida aos dados disponíveis de forma a poder tornar-se genérica, mas ao mesmo tempo permitir ter uma aplicabilidade prática. O objectivo da ferramenta informática desenvolvida é apresentar de forma imediata os custos da implementação que um sistema híbrido pode acarretar, dependendo apenas de três variáveis distintas. A primeira variável terá de ter em consideração o local de instalação do sistema. Em segundo lugar é o tipo de ligação (isolado ou ligado à rede) e, por fim, o custo dos equipamentos (eólico, solar e restantes componentes) que serão introduzidos. Após a inserção destes dados a aplicação informática apresenta valores estimados de Payback e VAL.

Energy resource management in smart grids considering an Intensive use of electric vehicles

The introduction of electricity markets and integration of Distributed Generation (DG) have been influencing the power system’s structure change. Recently, the smart grid concept has been introduced, to guarantee a more efficient operation of the power system using the advantages of this new paradigm. Basically, a smart grid is a structure that integrates different players, considering constant communication between them to improve power system operation and management. One of the players revealing a big importance in this context is the Virtual Power Player (VPP). In the transportation sector the Electric Vehicle (EV) is arising as an alternative to conventional vehicles propel by fossil fuels. The power system can benefit from this massive introduction of EVs, taking advantage on EVs’ ability to connect to the electric network to charge, and on the future expectation of EVs ability to discharge to the network using the Vehicle-to-Grid (V2G) capacity. This thesis proposes alternative strategies to control these two EV modes with the objective of enhancing the management of the power system. Moreover, power system must ensure the trips of EVs that will be connected to the electric network. The EV user specifies a certain amount of energy that will be necessary to charge, in order to ensure the distance to travel. The introduction of EVs in the power system turns the Energy Resource Management (ERM) under a smart grid environment, into a complex problem that can take several minutes or hours to reach the optimal solution. Adequate optimization techniques are required to accommodate this kind of complexity while solving the ERM problem in a reasonable execution time. This thesis presents a tool that solves the ERM considering the intensive use of EVs in the smart grid context. The objective is to obtain the minimum cost of ERM considering: the operation cost of DG, the cost of the energy acquired to external suppliers, the EV users payments and remuneration and penalty costs. This tool is directed to VPPs that manage specific network areas, where a high penetration level of EVs is expected to be connected in these areas. The ERM is solved using two methodologies: the adaptation of a deterministic technique proposed in a previous work, and the adaptation of the Simulated Annealing (SA) technique. With the purpose of improving the SA performance for this case, three heuristics are additionally proposed, taking advantage on the particularities and specificities of an ERM with these characteristics. A set of case studies are presented in this thesis, considering a 32 bus distribution network and up to 3000 EVs. The first case study solves the scheduling without considering EVs, to be used as a reference case for comparisons with the proposed approaches. The second case study evaluates the complexity of the ERM with the integration of EVs. The third case study evaluates the performance of scheduling with different control modes for EVs. These control modes, combined with the proposed SA approach and with the developed heuristics, aim at improving the quality of the ERM, while reducing drastically its execution time. The proposed control modes are: uncoordinated charging, smart charging and V2G capability. The fourth and final case study presents the ERM approach applied to consecutive days.

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.

Licensing endogenous cost-reduction in a differentiated Stackelberg model

In this paper we consider a differentiated Stackelberg model, when the leader firm engages in an R&D process that gives an endogenous cost-reducing innovation. The aim is to study the licensing of the cost-reduction by a two-part tariff. By using comparative static analysis, we conclude that the degree of the differentiation of the goods plays an important role in the results. We also do a direct comparison between our model and Cournot duopoly model.

Six thinking hats for electricity markets simulation: adapting meeting tools to agent decision

In almost all industrialized countries, the energy sector has suffered a severe restructuring that originated a greater complexity in market players’ interactions. The complexity that these changes brought made way for the creation of decision support tools that facilitate the study and understanding of these markets. MASCEM – “Multiagent Simulator for Competitive Electricity Markets” arose in this context providing a framework for evaluating new rules, new behaviour, and new participants in deregulated electricity markets. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. ALBidS is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This tool’s goal is to force the thinker to move outside his habitual thinking style. It was developed to be used mainly at meetings in order to “run better meetings, make faster decisions”. This dissertation presents a study about the applicability of the Six Thinking Hats technique in Decision Support Systems, particularly with the multiagent paradigm like the MASCEM simulator. As such this work’s proposal is of a new agent, a meta-learner based on STH technique that organizes several different ALBidS’ strategies and combines the distinct answers into a single one that, expectedly, out-performs any of them.