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.

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.

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.

Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market

In the proposed model, the independent system operator (ISO) provides the opportunity for maintenance outage rescheduling of generating units before each short-term (ST) time interval. Long-term (LT) scheduling for 1 or 2 years in advance is essential for the ISO and the generation companies (GENCOs) to decide their LT strategies; however, it is not possible to be exactly followed and requires slight adjustments. The Cournot-Nash equilibrium is used to characterize the decision-making procedure of an individual GENCO for ST intervals considering the effective coordination with LT plans. Random inputs, such as parameters of the demand function of loads, hourly demand during the following ST time interval and the expected generation pattern of the rivals, are included as scenarios in the stochastic mixed integer program defined to model the payoff-maximizing objective of a GENCO. Scenario reduction algorithms are used to deal with the computational burden. Two reliability test systems were chosen to illustrate the effectiveness of the proposed model for the ST decision-making process for future planned outages from the point of view of a GENCO.

Distribution system operation supported by contextual energy resource management based on intelligent SCADA

Future distribution systems will have to deal with an intensive penetration of distributed energy resources ensuring reliable and secure operation according to the smart grid paradigm. SCADA (Supervisory Control and Data Acquisition) is an essential infrastructure for this evolution. This paper proposes a new conceptual design of an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). This SCADA model is used to support the energy resource management undertaken by a distribution network operator (DNO). Resource management considers all the involved costs, power flows, and electricity prices, allowing the use of network reconfiguration and load curtailment. Locational Marginal Prices (LMP) are evaluated and used in specific situations to apply Demand Response (DR) programs on a global or a local basis. The paper includes a case study using a 114 bus distribution network and load demand based on real data.

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.

Evaluation of dynamic pass-through of carbon prices into electricity prices – a cointegrated VECM analysis

This paper addresses the impact of the CO2 opportunity cost on the wholesale electricity price in the context of the Iberian electricity market (MIBEL), namely on the Portuguese system, for the period corresponding to the Phase II of the European Union Emission Trading Scheme (EU ETS). In the econometric analysis a vector error correction model (VECM) is specified to estimate both long–run equilibrium relations and short–run interactions between the electricity price and the fuel (natural gas and coal) and carbon prices. The model is estimated using daily spot market prices and the four commodities prices are jointly modelled as endogenous variables. Moreover, a set of exogenous variables is incorporated in order to account for the electricity demand conditions (temperature) and the electricity generation mix (quantity of electricity traded according the technology used). The outcomes for the Portuguese electricity system suggest that the dynamic pass–through of carbon prices into electricity prices is strongly significant and a long–run elasticity was estimated (equilibrium relation) that is aligned with studies that have been conducted for other markets.

Bertrand oligopoly when rivals' costs are unknown

We study a Bertrand oligopoly model with incomplete information about rivals' costs, where the uncertainty is given by a uniform distribution. We compute the Bayesian-Nash equilibrium of this game, the ex-ante expected profit and the ex-post profit of each firm. We see that, even though only one firm produces in equilibrium, all firms have a positive ex-ante expected profit.

Bertrand and Cournot oligopolies when rivals' costs are unknown

We study Bertrand and Cournot oligopoly models with incomplete information about rivals’ costs, where the uncertainty is given by a uniform distribution. We compute the Bayesian- Nash equilibrium of both games, the ex-ante expected profits and the ex-post profits of each firm. We see that, in the price competition, even though only one firm produces in equilibrium, all firms have a positive ex-ante expected profit.

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.

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.

Autoconsumo fotovoltaico, um elemento de eficiência energética. Caso de estudo: ISEP

Mestrado em Energias Sustentáveis

Estudo da Viabilidade Técnico-Económica de umm Sistema de Cogeração para um Complexo de Piscinas Interiores

A presente dissertação centrou-se no estudo técnico-económico de dois cenários futuros para a continuação de fornecimento de energia térmica a um complexo de piscinas existente na região do vale do Tâmega. Neste momento a central de cogeração existente excedeu a sua licença de utilização e necessita de ser substituída. Os dois cenários em estudo são a compra de uma nova caldeira, a gás natural, para suprir as necessidades térmicas da caldeira existente a fuelóleo, ou o uso de um sistema de cogeração compacto que poderá estar disponível numa empresa do grupo. No primeiro cenário o investimento envolvido é cerca de 456 640 € sem proveitos de outra ordem para além dos requisitos térmicos, mas no segundo cenário os resultados são bem diferentes, mesmo que tenha de ser realizado o investimento de 1 000 000 € na instalação. Para este cenário foi efetuado um levantamento da legislação nacional no que toca à cogeração, recolheram-se dados do edifício como: horas de funcionamento, número de utentes, consumos de energia elétrica, térmica, água, temperatura da água das piscinas, temperatura do ar da nave, assim como as principais características da instalação de cogeração compacta. Com esta informação realizou-se o balanço de massa e energia e criou-se um modelo da nova instalação em software de modelação processual (Aspen Plus® da AspenTech). Os rendimentos térmico e elétrico obtidos da nova central de cogeração compacta foram, respetivamente, de 38,1% e 39,8%, com uma percentagem de perdas de 12,5% o que determinou um rendimento global de 78%. A avaliação da poupança de energia primária para esta instalação de cogeração compacta foi de 19,6 % o que permitiu concluir que é de elevada eficiência. O modelo criado permitiu compreender as necessidades energéticas, determinar alguns custos associados ao processo e simular o funcionamento da unidade com diferentes temperaturas de ar ambiente (cenários de verão e inverno com temperaturas médias de 20ºC e 5ºC). Os resultados revelaram uma diminuição de 1,14 €/h no custo da electricidade e um aumento do consumo de gás natural de 62,47 €/h durante o período mais frio no inverno devido ao aumento das perdas provocadas pela diminuição da temperatura exterior. Com esta nova unidade de cogeração compacta a poupança total anual pode ser, em média, de 267 780 € admitindo um valor para a manutenção de 97 698 €/ano. Se assim for, o projeto apresenta um retorno do investimento ao fim de 5 anos, com um VAL de 1 030 430 € e uma taxa interna de rentabilidade (TIR) de 14% (positiva, se se considerar a taxa de atualização do investimento de 3% para 15 anos de vida). Apesar do custo inicial ser elevado, os parâmetros económicos mostram que o projeto tem viabilidade económica e dará lucro durante cerca de 9 anos.

Defining Electricity Tariffs Using the Knowledge About the Consumers Profiles in ELECON Project

The increasing importance of the integration of distributed generation and demand response in the power systems operation and planning, namely at lower voltage levels of distribution networks and in the competitive environment of electricity markets, leads us to the concept of smart grids. In both traditional and smart grid operation, non-technical losses are a great economic concern, which can be addressed. In this context, the ELECON project addresses the use of demand response contributions to the identification of non-technical losses. The present paper proposes a methodology to be used by Virtual Power Players (VPPs), which are entities able to aggregate distributed small-size resources, aiming to define the best electricity tariffs for several, clusters of consumers. A case study based on real consumption data demonstrates the application of the proposed methodology.