A novel incentive-based demand response model for Cournot competition in electricity markets

This paper presents an analysis of competition between generators when incentive-based demand response is employed in an electricity market. Thermal and hydropower generation are considered in the model. A smooth inverse demand function is designed using a sigmoid and two linear functions for modeling the consumer preferences under incentive-based demand response program. Generators compete to sell energy bilaterally to consumers and system operator provides transmission and arbitrage services. The profit of each agent is posed as an optimization problem, then the competition result is found by solving simultaneously Karush-Kuhn-Tucker conditions for all generators. A Nash-Cournot equilibrium is found when the system operates normally and at peak demand times when DR is required. Under this model, results show that DR diminishes the energy consumption at peak periods, shifts the power requirement to off-peak times and improves the net consumer surplus due to incentives received for participating in DR program. However, the generators decrease their profit due to the reduction of traded energy and market prices

Multi-agent architecture for local electricity trading in power distribution systems

[ES] En la última década, los mercados eléctricos han desarrollado entornos competitivos para sistemas eléctricos completos. El rápido crecimiento de los recursos energéticos distribuidos ha dificultado mantener la credibilidad y estabilidad del sistema. Sin embargo, debido a la volatilidad de los recursos energéticos distribuidos las estrategias convencionales de gestión de la energía son incapaces de resolver estos problemas de forma centralizada. Además, los mercados centralizados de electricidad no son capaces de adaptarse al comportamiento flexible de los consumidores que ocurre en los programas de respuesta de demanda. Por lo tanto, se requieren nuevas estructuras de comercio de electricidad que proporcionen energía a las redes de distribución de forma descentralizada y distribuida. Este trabajo presenta un enfoque ascendente de gestión energética basado en una arquitectura multiagente para el comercio local de la electricidad. La estructura propuesta consiste en una clase de organización basada en sistemas multiagente, en la cual cada agente cumple diferentes tareas. Estos agentes est_an formados por recursos energéticos distribuidos, consumidores eléctricos, prosumidores, vehículos eléctricos (Electricit Vehicles (EV)), agregadores, un operador del sistema de distribución, coordinadores locales y los coordinadores de los EV del sistema. Además, proponemos un enfoque ascendente para el comercio de energía desde los usuarios finales, como agentes prosumidores capaces de proporcionar transacciones energéticas bidireccionales a los agregadores y al gestor de la red de distribución (Distibution System Operator (DSO)). En este contexto, se presenta una arquitectura basada en sistemas multiagente, para el sistema eléctrico de las casas inteligentes (como ejemplo de usuario final). A continuación, se define el sistema de gestión de la energía en el hogar (HEMS por sus siglas en ingles) para modelar el comportamiento flexible de los usuarios finales residenciales y su incertidumbre basándose en diferentes métodos de optimización (por ejemplo, intervalo, estocástico e intervalo-estocástico). Además, presentamos un método basado en escenarios probabilísticos para la gestión de la energía residencial y el comercio de energía con el mercado local de electricidad basado en una estrategia de licitación óptima. De acuerdo con nuestro modelo de oferta óptimo, el HEMS es capaz de realizar transacciones de energía con otros actores en su vecindario como un agente de fijación de precios basado en los enfoques de intercambio de energía entre pares o enfoques basados en la comunidad. Conforme al enfoque ascendente propuesto en nuestro trabajo de doctorado, las decisiones de los agentes en la capa inferior tienen prioridad en comparación con las decisiones de los agentes en las capas superiores. De esta manera, la estrategia propuesta gestiona la energía localmente para lograr una optimización social global. Además, en la red de distribución se pueden comercializar localmente diferentes tipos de productos básicos de electricidad, como la energía y la flexibilidad. A continuación, hemos propuesto varios enfoques (por ejemplo, descentralizado, monopolístico y basado en juegos) para la gestión de la flexibilidad energética entre los agentes de la red de distribución de energía, teniendo en cuenta el comportamiento flexible de los usuarios finales y los agregadores. Por último, se ha estudiado el impacto de los futuros sistemas de transporte en las redes inteligentes. Así, la gestión de la flexibilidad energética de los usuarios finales y las operaciones de recarga de los vehículos eléctricos se modelan en la red de distribución. Se han presentado tres estrategias de gestión de la energía para abordar la flexibilidad energética y el funcionamiento de los vehículos eléctricos entre los actores de la capa inferior del sistema eléctrico. Además, la incertidumbre causada por la movilidad de los vehículos eléctricos se ha modelado mediante una programación estocástica. Aquí, el reto es modelar un problema multinivel basado en la función objetiva de los agentes considerando la incertidumbre de los parámetros estocásticos del sistema. De esta forma, cada agente puede participar en diferentes tipos de transacciones eléctricas según sus funciones objetivas correspondientes. Se evalúa el rendimiento del sistema propuesto de gestión de la energía en el hogar (HEMS) comparándolo con los métodos de optimización de intervalos estocásticos propuestos y de bandas estocásticas predichas medicadas. Evaluamos el impacto del modelo de flexibilidad energética y su exactitud de predicción. Además, evaluamos el programa de respuesta de demanda en términos de las ganancias esperadas, de la energía eléctrica tramitada y de la credibilidad de los resultados. Para ello, proponemos un modelo de oferta óptima para el sistema de gestión de la energía en el hogar. Así, el sistema puede participar en el comercio local de electricidad. El rendimiento del modelo de oferta _optima propuesto se evalúa en dos casos diferentes. El Caso 1 evalúa el impacto de los coeficientes de optimismo y flexibilidad en el HEMS, considerando la estrategia de licitación óptima. En el caso 2, sin embargo, el rendimiento de los dos métodos de optimización diferentes -llamados InterStoch e Hybrid- en el HEMS se evalúa sin considerar la estrategia de licitación _optima. Posteriormente, se evalúa el funcionamiento de nuestros enfoques descentralizados, monopolísticos y basados en juegos en términos de su impacto en la incertidumbre de la línea de distribución y el comportamiento flexible de los usuarios finales. Por último, modelamos la gestión de la flexibilidad energética de los usuarios finales y la operación de carga de los EV en la red de distribución. Se presentan tres estrategias de gestión de la energía para abordar la flexibilidad energética y el funcionamiento de los EV entre los actores de la capa inferior del sistema eléctrico

Towards transactive energy systems: An analysis on current trends

This paper presents a comprehensive analysis on the latest advances in transactive energy systems. The main contribution of this work is centered on the definition of transactive energy concepts and how such systems can be implemented in the smart grid paradigm. The analyzed works have been categorized into three lines of research: (i) transactive network management; (ii) transactive control; and (iii) peer-to-peer markets. It has been found that most of the current approaches for transactive energy are available as a model, lacking the real implementation to have a complete validation. For that purpose, both scientific and practical aspects of transactive energy should be studied in parallel, implementing adequate simulation platforms and tools to scrutiny the results.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

Application of demand response programs for peak reduction using load aggregator

O aumento do consumo de energia requer atenção. Os especialistas propuseram muitas soluções para otimizar o uso de energia e propõem um sistema de gestão de energia eficiente. No entanto, desenvolver um sistema de energia que contempla agregadores de carga é óbvio para aprimorar o processo de gestão de energia. Este trabalho discute um sistema de gestão de energia para implementar programas de Demand Response (DR) usando abordagens de agregação de carga. Neste trabalho, dois estudos de caso comparam as diferentes respostas do sistema. O objetivo principal é discutir o papel de diferentes modelos de agregador de carga no sistema de energia, implementando um programa de DR. Esses agregadores de carga controlam diferentes tipos de cargas. Neste contexto, vários tipos de cargas domésticas são consideradas cargas controláveis. No processo de agregação, o objetivo é agregar as cargas que possuem as mesmas características usando a análise de agrupamento das cargas. A contribuição científica desta dissertação está relacionada com a redução da ponta e a agregação de cargas, considerando as cargas controláveis e os recursos de geração no sistema. Para atingir o objetivo anterior, foram realizados dois estudos de caso. Cada estudo de caso consiste em três cenários baseados no modelo de agregação de carga. Os resultados dos estudos indicam as respostas do sistema aos diferentes cenários e ilustram os méritos do modelo de agregador de carga. Além disso, os resultados demonstram como o agrupamento dos dispositivos de carga no sistema pode efetivamente fornecer redução de pico com recurso a programas de DR.The increment of energy consumption takes a high level of attention. The experts have proposed many solutions to optimize energy use and propose an efficient energy management system. However, verifying the load aggregators' role energy system is obvious to enhance the energy management process. This work discusses an energy management system to implement DR programs using load aggregation approaches. In this work, two case studies compare the different responses of the system. The main goal is to discuss the role of different load aggregator models in the power system by implementing a DR program. Those load aggregators control different types of loads. In this context, various types of domestic loads are considered controllable loads. In the aggregation process, the goal is to aggregate the loads that have the same features using the clustering analysis of the loads. The scientific contribution of this thesis is related to the integration of providing the peak reduction and the clustered aggregation of loads, considering the controllable loads and generation resources in the system. To achieve the previous goal, two case studies have been done. Each case study consists of three scenarios based on the load aggregation model. The results of the case studies indicate the system responses to the different scenarios and illustrate the merits of the load aggregator model. Furthermore, the results demonstrate how clustering the load appliances in the system can effectively provide peak reduction due to the DR programs

Heuristic optimization of clusters of heat pumps: A simulation and case study of residential frequency reserve

The technological challenges of adapting energy systems to the addition of more renewables are intricately interrelated with the ways in which markets incentivize their development and deployment. Households with own onsite distributed generation augmented by electrical and thermal storage capacities (prosumers), can adjust energy use based on the current needs of the electricity grid. Heat pumps, as an established technology for enhancing energy efficiency, are increasingly seen as having potential for shifting electricity use and contributing to Demand Response (DR). Using a model developed and validated with monitoring data of a household in a plus-energy neighborhood in southern Germany, the technical and financial viability of utilizing household heat pumps to provide power in the market for Frequency Restoration Reserve (FRR) are studied. The research aims to evaluate the flexible electrical load offered by a cluster of buildings whose heat pumps are activated depending on selected rule-based participation strategies. Given the prevailing prices for FRR in Germany, the modelled cluster was unable to reduce overall electricity costs and thus was unable to show that DR participation as a cluster with the heat pumps is financially viable. Five strategies that differed in the respective contractual requirements that would need to be agreed upon between the cluster manager and the aggregator were studied. The relatively high degree of flexibility necessary for the heat pumps to participate in FRR activations could be provided to varying extents in all strategies, but the minimum running time of the heat pumps turned out to be the primary limiting physical (and financial) factor. The frequency, price and duration of the activation calls from the FRR are also vital to compensate the increase of the heat pumps’ energy use. With respect to thermal comfort and self-sufficiency constraints, the buildings were only able to accept up to 34% of the activation calls while remaining within set comfort parameters. This, however, also depends on the characteristics of the buildings. Finally, a sensitivity analysis showed that if the FRR market changed and the energy prices were more advantageous, the proposed approaches could become financially viable. This work suggests the need for further study of the role of heat pumps in flexibility markets and research questions concerning the aggregation of local clusters of such flexible technologies.Comisión Europea 69596

Foresighted Demand Side Management

We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators' costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators' states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators' states or the ISO's state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregator's energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities