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

Portfolio Optimization for Electricity Market Participation with NPSO-LRS

Massive changes in electricity markets have occurred during the last years, as a consequence of the massive introduction of renewable energies. These changes have led to a restructuring process that had an impact throughout the electrical industry. The case of the electricity markets is a relevant example, where new forms of trading emerged and new market entities were created. With these changes, the complexity of electricity markets increased as well, which brought out the need from the involved players for adequate support to their decision making process. Artificial intelligence plays an important role in the development of these tools. Multi-agent systems, in particular, have been largely explored by stakeholders in the sector. Artificial intelligence also provides intelligent solutions for optimization, which enable troubleshooting in a short time and with very similar results to those achieved by deterministic techniques, which usually result from very high execution times. The work presented in this paper aims at solving a portfolio optimization problem for electricity markets participation, using an approach based on NPSO-LRS (New Particle Swarm Optimization with Local Random Search). The proposed method is used to assist decisions of electricity market players.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/00760/2013info:eu-repo/semantics/publishedVersio

Hybrid particle swarm optimization of electricity market participation portfolio

This paper proposes a novel hybrid particle swarm optimization methodology to solve the problem of optimal participation in multiple electricity markets. The decision time is usually very important when planning the participation in electricity markets. This environment is characterized by the time available to take action, since different electricity markets have specific rules, which requires participants to be able to adapt and plan their decisions in a short time. Using metaheuristic optimization, participants' time problems can be resolved, because these methods enable problems to be solved in a short time and with good results. This paper proposes a hybrid resolution method, which is based on the particle swarm optimization metaheuristic. An exact mathematical method, which solves a simplified, linearized, version of the problem, is used to generate the initial solution for the metaheuristic approach, with the objective of improving the quality of results without representing a significant increase of the execution time.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT) and No 641794 (project DREAM-GO); NetEfficity Project (P2020 − 18015); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE pro-gram and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Decision Support for Negotiations among Microgrids Using a Multiagent Architecture

[EN] This paper presents a decision support model for negotiation portfolio optimization considering the participation of players in local markets (at the microgrid level) and in external markets, namely in regional markets, wholesale negotiations and negotiations of bilateral agreements. A local internal market model for microgrids is defined, and the connection between interconnected microgrids is based on nodal pricing to enable negotiations between nearby microgrids. The market environment considering the local market setting and the interaction between integrated microgrids is modeled using a multi-agent approach. Several multi-agent systems are used to model the electricity market environment, the interaction between small players at a microgrid scale, and to accommodate the decision support features. The integration of the proposed models in this multi-agent society and interaction between these distinct specific multi-agent systems enables modeling the system as a whole and thus testing and validating the impact of the method in the outcomes of the involved players. Results show that considering the several negotiation opportunities as complementary and making use of the most appropriate markets depending on the expected prices at each moment allows players to achieve more profitable results

Hybrid approach based on particle swarm optimization for electricity markets participation

In many large-scale and time-consuming problems, the application of metaheuristics becomes essential, since these methods enable achieving very close solutions to the exact one in a much shorter time. In this work, we address the problem of portfolio optimization applied to electricity markets negotiation. As in a market environment, decision-making is carried out in very short times, the application of the metaheuristics is necessary. This work proposes a Hybrid model, combining a simplified exact resolution of the method, as a means to obtain the initial solution for a Particle Swarm Optimization (PSO) approach. Results show that the presented approach is able to obtain better results in the metaheuristic search process.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 and Ricardo Faia is supported by FCT Funds through and SFRH/BD/133086/2017 PhD scholarship.info:eu-repo/semantics/publishedVersio

Opening of Ancillary Service Markets to Distributed Energy Resources: A Review

Electric power systems are moving toward more decentralized models, where energy generation is performed by small and distributed power plants, often from renewables. With the gradual phase out from fossil fuels, however, Distribution Energy Resources (DERs) are expected to take over in the provision of all regulation services required to operate the grid. To this purpose, the opening of national Ancillary Service Markets (ASMs) to DERs is considered an essential passage. In order to allow this transition to happen, current opportunities and barriers to market participation of DERs must be clearly identified. In this work, a comprehensive review is provided of the state-of-the-art of research on DER integration into ASMs. The topic at hand is analyzed from different perspectives. First, the current situation and main trends regarding the reformation processes of national ASMs are analyzed to get a clear picture of the evolutions expected and adjustment required in the future, according to the scientific community. Then, the focus is moved to the strategies to be adopted by aggregators for the effective control and coordination of DERs, exploring the challenges posed by the uncertainties affecting the problem. Coordination schemes between transmission and distribution system operators, and the implications on the grid infrastructure operation and planning, are also investigated. Finally, the review deepens the control capabilities required for DER technologies to perform the needed control actions

Information requirements for strategic decision making: energy market

Over the last two decades, the electricity sector has been involved in a challenging restructuring process in which the vertical integrated structure (monopoly) is being replaced by a horizontal set of companies. The growing supply of electricity, flowing in response to free market pricing at the wellhead, led to increased competition. In the new framework of deregulation, what characterizes the electric industry is a commodity wholesale electricity marketplace. This new environment has drastically changed the objective of electricity producing companies. In the vertical integrated industry, utilities were forced to meet all the demand from customers living in a certain region at fixed rates. Then, the operation of the Generation Companies (GENCOs) was centralized and a single decision maker allocated the energy services by minimizing total production costs. Nowadays, GENCOs are involved not only in the electricity market but also in additional markets such as fuel markets or environmental markets. A gas or coal producer may have fuel contracts that define the production limit over a time horizon. Therefore, producers must observe this price levels in these other markets. This is a lesson we learned from the Electricity Crisis in California. The Californian market\u27s collapse was not the result of market decentralization but it was triggered by other decisions, such as high natural gas prices, with a direct impact in the supply-demand chain. This dissertation supports generation asset business decisions -from fuel supply concerns to wholesale trading in energy and ancillary services. The forces influencing the value chain are changing rapidly, and can become highly controversial. Through this report, the author brings an integrated and objective perspective, providing a forum to identify and address common planning and operational needs. The purpose of this dissertation is to present theories and ideas that can be applied directly in algorithms to make GENCOs decisions more efficient. This will decompose the problem into independent subproblems for each time interval. This is preferred because building a complete model in one time is practically impossible. The diverse scope of this report is unified by the importance of each topic to understanding or enhancing the profitability of generation assets. Studies of top strategic issues will assess directly the promise and limits to profitability of energy trading. Studies of ancillary services will permit companies to realistically gauge the profitability of different services, and develop bidding strategies tuned to competitive markets

AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).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/00760/2019info:eu-repo/semantics/publishedVersio

Decision support for participation in electricity markets considering the transaction of services and electricity at the local level

[EN] The growing concerns regarding the lack of fossil fuels, their costs, and their impact on the environment have led governmental institutions to launch energy policies that promote the increasing installation of technologies that use renewable energy sources to generate energy. The increasing penetration of renewable energy sources brings a great fluctuation on the generation side, which strongly affects the power and energy system management. The control of this system is moving from hierarchical and central to a smart and distributed approach. The system operators are nowadays starting to consider the final end users (consumers and prosumers) as a part of the solution in power system operation activities. In this sense, the end-users are changing their behavior from passive to active players. The role of aggregators is essential in order to empower the end-users, also contributing to those behavior changes. Although in several countries aggregators are legally recognized as an entity of the power and energy system, its role being mainly centered on representing end-users in wholesale market participation. This work contributes to the advancement of the state-of-the-art with models that enable the active involvement of the end-users in electricity markets in order to become key participants in the management of power and energy systems. Aggregators are expected to play an essential role in these models, making the connection between the residential end-users, electricity markets, and network operators. Thus, this work focuses on providing solutions to a wide variety of challenges faced by aggregators. The main results of this work include the developed models to enable consumers and prosumers participation in electricity markets and power and energy systems management. The proposed decision support models consider demand-side management applications, local electricity market models, electricity portfolio management, and local ancillary services. The proposed models are validated through case studies based on real data. The used scenarios allow a comprehensive validation of the models from different perspectives, namely end-users, aggregators, and network operators. The considered case studies were carefully selected to demonstrate the characteristics of each model, and to demonstrate how each of them contributes to answering the research questions defined to this work.[ES] La creciente preocupación por la escasez de combustibles fósiles, sus costos y su impacto en el medio ambiente ha llevado a las instituciones gubernamentales a lanzar políticas energéticas que promuevan la creciente instalación de tecnologías que utilizan fuentes de energía renovables para generar energía. La creciente penetración de las fuentes de energía renovable trae consigo una gran fluctuación en el lado de la generación, lo que afecta fuertemente la gestión del sistema de potencia y energía. El control de este sistema está pasando de un enfoque jerárquico y central a un enfoque inteligente y distribuido. Actualmente, los operadores del sistema están comenzando a considerar a los usuarios finales (consumidores y prosumidores) como parte de la solución en las actividades de operación del sistema eléctrico. En este sentido, los usuarios finales están cambiando su comportamiento de jugadores pasivos a jugadores activos. El papel de los agregadores es esencial para empoderar a los usuarios finales, contribuyendo también a esos cambios de comportamiento. Aunque en varios países los agregadores están legalmente reconocidos como una entidad del sistema eléctrico y energético, su papel se centra principalmente en representar a los usuarios finales en la participación del mercado mayorista. Este trabajo contribuye al avance del estado del arte con modelos que permiten la participación activa de los usuarios finales en los mercados eléctricos para convertirse en participantes clave en la gestión de los sistemas de potencia y energía. Se espera que los agregadores desempeñen un papel esencial en estos modelos, haciendo la conexión entre los usuarios finales residenciales, los mercados de electricidad y los operadores de red. Por lo tanto, este trabajo se enfoca en brindar soluciones a una amplia variedad de desafíos que enfrentan los agregadores. Los principales resultados de este trabajo incluyen los modelos desarrollados para permitir la participación de los consumidores y prosumidores en los mercados eléctricos y la gestión de los sistemas de potencia y energía. Los modelos de soporte de decisiones propuestos consideran aplicaciones de gestión del lado de la demanda, modelos de mercado eléctrico local, gestión de cartera de electricidad y servicios auxiliares locales. Los modelos propuestos son validan mediante estudios de casos basados en datos reales. Los escenarios utilizados permiten una validación integral de los modelos desde diferentes perspectivas, a saber, usuarios finales, agregadores y operadores de red. Los casos de estudio considerados fueron cuidadosamente seleccionados para demostrar las características de cada modelo y demostrar cómo cada uno de ellos contribuye a responder las preguntas de investigación definidas para este trabajo