Differential Evolution Aplication in Portfolio optimization for Electricity Markets

Smart Grid technologies enable the intelligent integration and management of distributed energy resources. Also, the advanced communication and control capabilities in smart grids facilitate the active participation of aggregators at different levels in the available electricity markets. The portfolio optimization problem consists in finding the optimal bid allocation in the different available markets. In this scenario, the aggregator should be able to provide a solution within a timeframe. Therefore, the application of metaheuristic approaches is justified, since they have proven to be an effective tool to provide near-optimal solutions in acceptable execution times. Among the vast variety of metaheuristics available in the literature, Differential Evolution (DE) is arguably one of the most popular and successful evolutionary algorithms due to its simplicity and effectiveness. In this paper, the use of DE is analyzed for solving the portfolio optimization problem in electricity markets. Moreover, the performance of DE is compared with another powerful metaheuristic, the Particle Swarm optimization (PSO), showing that despite both algorithms provide good results for the problem, DE overcomes PSO in terms of quality of the solutions.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 and grant agreement No 703689 (project ADAPT);info:eu-repo/semantics/publishedVersio

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

GA Optimization Technique for Portfolio Optimization of Electricity Market Participation

This paper presents a methodology based on genetic Algorithms (GA) to solve the problem of optimal participation in multiple electricity markets. With the emergence of new requirements for electrical power markets, it has become fundamental to develop tools to aid in decision making, understanding the functioning of markets and forecast iterations that occur between the different entities in the market. Artificial intelligence plays a crucial role in the development of these tools. Using artificial intelligence techniques, it is possible to simulate the different existing players in the market, to enable these players to be adaptive to any situation, and to model any type of trading. Artificial intelligence based metaheuristic optimization tools allow solving problems in a short time, and with very close results to those that deterministic techniques are able to achieve, at the cost of a high execution time. The achieved results, using a simulation scenario based on real data from the Iberian electricity market, show that the proposed method is able to reach better results than previous implementations of a Particle Swarm Optimization (PSO) and a Simulated Annealing (SA) methods, while achieving very similar objective function results to those of a deterministic approach, in a much faster execution time.info:eu-repo/semantics/publishedVersio