Contextual Simulated Annealing Q-Learning for Pre-negotiation of Agent-Based Bilateral Negotiations

Electricity markets are complex environments, which have been suffering continuous transformations due to the increase of renewable based generation and the introduction of new players in the system. In this context, players are forced to re-think their behavior and learn how to act in this dynamic environment in order to get as much benefit as possible from market negotiations. This paper introduces a new learning model to enable players identifying the expected prices of future bilateral agreements, as a way to improve the decision-making process in deciding the opponent players to approach for actual negotiations. The proposed model introduces a con-textual dimension in the well-known Q-Learning algorithm, and includes a simulated annealing process to accelerate the convergence process. The proposed model is integrated in a multi-agent decision support system for electricity market players negotiations, enabling the experimentation of results using real data from the Iberian electricity market.This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019.info:eu-repo/semantics/publishedVersio

Optimization of Electricity Markets Participation with Simulated Annealing

The electricity markets environment has changed completely with the introduction of renewable energy sources in the energy distribution systems. With such alterations, preventing the system from collapsing required the development of tools to avoid system failure. In this new market environment competitiveness increases, new and different power producers have emerged, each of them with different characteristics, although some are shared for all of them, such as the unpredictability. In order to battle the unpredictability, the power supplies of this nature are supported by techniques of artificial intelligence that enables them crucial information for participation in the energy markets. In electricity markets any player aims to get the best profit, but is necessary have knowledge of the future with a degree of confidence leading to possible build successful actions. With optimization techniques based on artificial intelligence it is possible to achieve results in considerable time so that producers are able to optimize their profits from the sale of Electricity. Nowadays, there are many optimization problems where there are no that cannot be solved with exact methods, or where deterministic methods are computationally too complex to implement. Heuristic optimization methods have, thus, become a promising solution. In this paper, a simulated annealing based approach is used to solve the portfolio optimization problem for multiple electricity markets participation. A case study based on real electricity markets data is presented, and the results using the proposed approach are compared to those achieved by a previous implementation using particle swarm optimization.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794info:eu-repo/semantics/publishedVersio

An application of modern processor systems in the field of electrical controlled drives

Hlavní náplní předložené disertační práce je návrh a realizace nového konceptu řídicího systému pro řízení elektrických regulovaných pohonů a menších měničových systémů výkonové elektroniky, který je zároveň schopen aplikace výpočetně náročných algoritmů založených na metodách „soft-computing“, opírajících se o teorii umělé neuronové sítě, fuzzy logiky, a především genetického algoritmu, který je předmětem implementace reálné aplikace pro ověření nově navrženého řídicího systému. První část práce je zaměřena na stručný přehled současného hardwarového vývoje na poli řídicích systému a definuje zde obecné nároky na řídicí systémy pro výkonovou elektroniku a elektrické regulované pohony. Dále pak seznamuje s definicí konceptu návrhu a vývoje prototypu nového modulárního řídicího systému s moderním dvoujádrovým DSC firmy Texas Instruments rodiny Delfino TMS320, řady F2837xD. Druhá část práce stručně seznamuje s přehledem obecných metod bezsensorového řízení, dále je věnována obecnému přehledu meta-heuristických metod optimalizace, kde je podrobněji popsána problematika genetického algoritmu, jako zástupce podskupiny evolučních algoritmů meta-heuristických metod optimalizace. Následující části jsou věnovány aplikacím genetického algoritmu ve struktuře elektrických regulovaných pohonů a jsou zde prezentovány dosažené simulační výsledky. Poslední část práce předkládá podrobný popis a analýzu struktury reálné implementace genetického algoritmu v nově navrženém řídicím systému, rozdělení řídicího algoritmu mezi mikroprocesory a dosažené výsledky experimentálního ověření reálné aplikace genetického algoritmu ve struktuře bezsensorového řízení elektrického regulovaného pohonu s asynchronním motorem.The main content of the doctoral thesis is the design and implementation of a new control system for controlling electrical controlled drives and small converter systems of the power electronics, which is also capable of applying computationally demanding algorithms based on soft computing methods based on the theory of artificial neural network, Fuzzy logic, and especially the Genetic Algorithm, which is the subject of the implementation of a real application to verify a newly designed control system. The first part of the article is focused on a brief overview of current Hardware developments in the field of control systems and defines the general requirements for control systems for power electronics and electrical controlled drives. It also introduces the definition of the concept of design and development of a prototype of a new modular control system with a modern dual-core DSC from Texas Instruments Delfino TMS320 family, F2837xD series. The second part briefly introduces an overview of general methods of sensorless control, and is also devoted to a general overview of Meta-Heuristic optimization methods, where the problem of Genetic Algorithm is described in more detail, as a representative of a subset of evolutionary algorithms of Meta-Heuristic optimization methods. The following sections are devoted to the applications of the Genetic Algorithm in the structure of Electrical Controlled Drives and the simulation results are presented here. The last part presents a detailed description and analysis of the structure a real implementation of the Genetic Algorithm in the newly designed modular control system, distribution of control algorithm between microprocessors cores, and achieved results of experimental verification of the real application of Genetic Algorithm in sensorless control of Electrical Controlled Drive with Induction motor.430 - Katedra elektronikyvyhově

Otimização de Portfólio de Participação em Mercados de Energia Elétrica

Na atualidade são visíveis as mudanças ocorridas nos mercados de energia elétrica, em consequência da introdução maciça de energia proveniente de fontes renováveis. Pelo facto de serem renováveis são de grande interesse para a população, pois o custo de produção e as emissões de gases, que contribuem para o efeito de estufa durante o seu funcionamento, são nulas. Estas características são essenciais para as mais altas chefias das instituições europeias, que impuseram políticas para promover a utilização e instalação de tecnologia para o aproveitamento das fontes que facultam as energias renováveis. Os estados membros europeus mostraram-se recetíveis a estas políticas e incentivaram o investimento nestas tecnologias. Deste modo, houve uma enorme introdução de energias de arater intermitente e instável que condicionaram o normal funcionamento dos sistemas de energia elétrica, o que, por sua vez, conduziu a inúmeras mudanças no setor. Esta reestruturação teve impacto em todo o setor, como é o caso dos mercados de energia elétrica, onde surgiram novas formas de negociação e foram criadas novas entidades de mercado. Com estas alterações, a complexidade dos mercados de energia elétrica aumentou, assim como a imprevisibilidade dos mesmos. Por isso, tornou-se essencial a existência de formas de apoio que auxilie a tomada de decisão por parte das entidades de mercado. Com a emergência de todas estas exigências, tornou-se fundamental o desenvolvimento de ferramentas para auxílio na tomada de decisão. Estas ferramentas ajudam as diversas entidades a perceber o funcionamento dos mercados e prever as interações que ocorrerão entre as diferentes entidades existentes no mercado. A inteligência artificial teve um papel crucial no desenvolvimento destas ferramentas, nomeadamente os sistemas multiagente, que têm sido uma solução muito explorada pelos interessados no setor. Estes, utilizam várias técnicas da inteligência artificial, o que lhes permite serem adaptativos a diferentes situações, simular os diferentes agentes existentes no mercado, permitir diversos tipos de negociação, e ainda aprender ao longo da sua utilização. No entanto, apesar de estas ferramentas atualmente estarem voltadas para o estudo do funcionamento do sistema elétrico, deixam de lado o contexto de negociação e descartam o apoio às decisões do vendedor/comprador de eletricidade. O largo âmbito de aplicação da inteligência artificial fornece diversas experiências, nomeadamente ferramentas de otimização meta-heurísticas, que permitem a resolução de problemas num curto espaço de tempo, e com uma qualidade de resultados muito próxima daquela alcançada por técnicas determinísticas à custa de um elevado tempo de execução. O trabalho desenvolvido nesta dissertação tem como objeto de estudo a falha supra referenciada. Sugere uma metodologia de negociação da energia elétrica que permite vender e comprar a mesma em diferentes mercados com regras específicas, e indica um portfólio de participação nos vários mercados em que cada interveniente pode negociar. A metodologia apresentada permite gerar cenários realistas a partir do resultado da otimização do portfólio, que podem ser tomados em consideração na decisão dos intervenientes de mercado, e assim conseguirem retirar o máximo proveito das suas negociações. Os resultados apresentados foram obtidos através da utilização de dados reais provenientes dos diferentes operadores de mercados. Estes dados são válidos para a formulação de diferentes cenários que possam ser considerados no ato da negociação.Nowadays, there are several relevant changes in electricity markets, which are a consequence of the massive introduction of renewable energies. The fact that they are renewable is of great interest for all of us, because the cost of production of this energy is null and emissions of greenhouse gases are also zero during operation. This feature aroused great interest in the high European institutions that have imposed policies to promote the use and installation of technology for the use of sources that provide renewable energy. European member states have shown receptiveness to these policies, potentiating the investment in these technologies and thus hearing a great introduction of intermittent and unstable energy that conditioned the normal operation of power systems and led to further inevitable changes in an already under-restructuring power and energy sector. This restructuring had an impact throughout the industry, as is the case of the electricity markets, where new forms of trading emerged and new market entities were created. With these changes the complexity of electricity markets increased as well as the associated unpredictability. This made is essential to have support tools to aid decision making by the arket entities. With the emergence of all these requirements it is fundamental to develop tools in order to assist the decision-making process, and to help understanding the functioning of markets and predict the interactions that occur between the existing market entities. Artificial intelligence has an important role in the development of these tools. Multi-agent systems, in particular, have been much explored by stakeholders in the sector as a valid solution. They use various techniques of artificial intelligence that allows them to be adaptive to any situation, to simulate the different existing players in the market, allowing any type of trading and enabling them to learn the logo of its use. However, these tools are directed to study of the proper functioning of the electrical system, leaving aside the negotiation context and the decision support for the seller / buyer of electricity. The applicability of artificial intelligence is not limited to electricity markets. It is also applied in many other areas due to its optimization tools that enable solving problems in a short time and with very similar results to those achieved by deterministic techniques, at the cost of a high execution time. The work in this dissertation addresses the above-mentioned gaps, and suggests an electricity trading decision support methodology to buy and sell electricity in different markets with specific rules. This is done by suggesting a portfolio of market participation that each party can perform. The presented methodology generates realistic scenarios from the portfolio optimization of the results that may be taken into account in the decision of market participants; and allow these players to take full advantage of it. The results were obtained through the use of real data stemmed from different market operators, which are valid for the generation of different scenarios that can be taken into account in the negotiation act