Local flexibility market design for aggregators providing multiple flexibility services at distribution network level

This paper presents a general description of local flexibility markets as a market-based management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids’ hosting capacity.Postprint (published version

Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization

On top of Smart Grid technologies and new market mechanism design, the further deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies, such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145686/1/Tao Chen Final Dissertation.pdfDescription of Tao Chen Final Dissertation.pdf : Dissertatio

Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

A comprehensive review on electric vehicles smart charging: solutions, strategies, technologies, and challenges

The role of electric vehicles (EVs) in energy systems will be crucial over the upcoming years due to their environmental-friendly nature and ability to mitigate/absorb excess power from renewable energy sources. Currently, a significant focus is given to EV smart charging (EVSC) solutions by researchers and industries around the globe to suitably meet the EVs' charging demand while overcoming their negative impacts on the power grid. Therefore, effective EVSC strategies and technologies are required to address such challenges. This review paper outlines the benefits and challenges of the EVSC procedure from different points of view. The role of EV aggregator in EVSC, charging methods and objectives, and required infrastructure for implementing EVSC are discussed. The study also deals with ancillary services provided by EVSC and EVs' load forecasting approaches. Moreover, the EVSC integrated energy systems, including homes, buildings, integrated energy systems, etc., are reviewed, followed by the smart green charging solutions to enhance the environmental benefit of EVs. The literature review shows the efficiency of EVSC in reducing charging costs by 30 %, grid operational costs by 10 %, and renewable curtailment by 40 %. The study gives key findings and recommendations which can be helpful for researchers and policymakers

Flexibility market for congestion management in smart grids

Mención Internacional en el título de doctorCurrent power systems are facing several sustainability challenges to meet the increasing demand of electricity. In addition, there is a global direction to increase the share of renewable energy sources in the power generation mix and energy efficiency. In the face of all such challenges, smart grids were incepted. Smart grids are modernized power systems that integrate state-of-the art communication and information technology to facilitate the bidirectional flow of information and electricity between the supply and demand sides. The resilience of smart grids can pave the way for having more flexibility at the distribution level of the power systems. Demand response (DR) programs are considered one of the sources of system flexibility and it is one of the main components of smart grids. DR can be defined as the willingness of customers to alter their electricity consumption profile in response to price signals. Transmission system operators have been implementing demand response programs in a straightforward fashion for several years now. For example, by having energy prices that are expensive during on-peak periods and low-priced at off-peak periods. Other type of DR programs introduces price signals when grid reliability is compromised and a reduction in energy consumption is necessary. In this way, customers can plan their activities accordingly in order to save money. Now, a new era of technology, artificial intelligence and the so-called “internet of things”, have provided new ways to explore the full potential of demand response, by allowing to alter loads in a much more dynamic and precise manner, thus optimizing the operation of grid assets. This thesis focuses on one of the main types of DR programs which is demand flexibility. Demand flexibility is the ability of the demand-side customers to adjust their load profiles in response to an external market signal. On the short- and medium-term periods, distribution system operators (DSOs) can take advantage of the flexibility of demand to mitigate network congestions caused by increased peaks or high penetration of renewable energy. On the long-term period, DSOs can include demand flexibility in their network expansion planning process for future demand growth. The optimal usage of demand flexibility can help in postponing needed investments for upgrading the networks’ capacity. Demand flexibility can be acquired through market-based solutions which can deliver cost-efficient flexibility services for several market agents by facilitating competition between different flexibility providers. Market mechanisms are considered by policy makers as the optimal solution for flexibility access. With respect to that, this thesis proposes a comprehensive framework for a distribution-level flexibility market, called “Flex-DLM” that enables and facilitates the trading of demand flexibility between the distribution system operator, as the main buyer, and aggregators, as sellers representing flexible consumers. Two types of demand flexibility services were modelled, which are: 1- Up-regulation flexibility (UREG), which corresponds to load decrease volumes, and 2- Down-regulation flexibility (DREG), which corresponds load increase volumes. In addition, the payback effect, which is a common event to the activation of demand flexibility, is considered for both types of flexibility services. Also, the distribution network constraints were modelled, which represents the power flow constraints of the network, which is key to present a realistic model for the flexibility market. In the Flex-DLM, the DSO is considered as the market operator who is responsible of clearing the market, while making sure the network congestions are mitigated. The Flex-DLM operates on two timeframes which are day-ahead and real-time with an objective to provide the DSO with flexibility products that can help it in the congestion management process. In addition to this, the uncertainty of demand is taken into consideration to prevent the DSO from procuring inaccurate amounts of demand flexibility. A new option is introduced in the day-ahead Flex-DLM, called the right-to-use (RtU) that allows the DSO to reserve the right to activate demand flexibility during the day-ahead period for congestions that have low probability of occurrence on the following operation day. In this way, the DSO can call upon this option in real-time if the congestion takes place. Also, the uncertainty behind the customers’ commitment to the flexibility activation requests and amounts is taken into consideration. In this thesis, the decision-making process of the DSO for optimizing its choice of demand flexibility and minimizing its total cost is modelled. Two methods were carried out for the optimization model proposed in this work. The first method follows a deterministic approach, where the objective is to optimize the DSO’s cost and clear the Flex-DLM during the day-ahead period only, without taking into account the uncertainty of demand and the uncertainty of consumers’ participation. The second method follows probabilistic approach, which considers the demand uncertainty during the day-ahead and real-time periods and models the uncertainty behind the customers’ commitment. Both optimization methods were integrated with an optimal power flow (OPF) solver tool in order to check the technical validity of the activated flexibility services and to make sure that the payback effect does not cause further congestions in the network. The advantage of the proposed framework is that it requires minimum regulatory changes and it does not involve the DSO in any electricity trading. Also, the proposed optimization method can be integrated with any OPF solver tool. Different distribution feeders obtained from a distribution network located in Spain were used to check the validity of the proposed framework and the decision-making process. The case studies are divided into two parts: 1- The first part applies the proposed flexibility framework from a deterministic perspective and 2- The second part applies the Flex-DLM framework considering all uncertainties, which corresponds to the probabilistic optimization approach. Finally, to help the DSO in the long-term planning process of its local network, a cost & benefit analysis is carried out to value the economic impact of implementing demand flexibility programs as an alternate solution to conventional network upgradesLos sistemas de energía actuales se enfrentan a varios desafíos de sostenibilidad para satisfacer la creciente demanda de electricidad. Además, existe una clara tendencia a aumentar la proporción de fuentes renovables de energía en la generación de energía y así como hacia la eficiencia energética. Como parte de la respuesta a estos desafíos, se iniciaron las redes inteligentes. Las redes inteligentes son sistemas de energía modernizados que integran tecnología de comunicación e información de última generación para facilitar el flujo bidireccional de información y electricidad entre la oferta y la demanda. La utilización de las redes inteligentes pretende facilitar el empleo de la flexibilidad en la red de distribución de los sistemas eléctricos. Los programas de gestión de la demanda se consideran una de las fuentes de flexibilidad del sistema y es uno de los puntos sobre los que se apoyan las redes inteligentes. La gestión de la demanda se puede definir como la disposición de los clientes a alterar su perfil de consumo de electricidad en respuesta a las señales de precios. Los operadores de sistemas de transporte han estado implementando programas de respuesta a la demanda de manera directa desde hace varios años. Por ejemplo, la diferencia entre precios altos y bajos en el mercado mayorista introduce un incentivo para el consumo en horas de menor precio. Otro tipo de programas de gestión de la demanda introduce señales de precios cuando la fiabilidad de la red se ve comprometida y es necesaria una reducción en el consumo de energía. De esta manera, los consumidores pueden planificar sus actividades en consecuencia para ahorrar costes. Ahora, una nueva era de la tecnología, la inteligencia artificial y el llamado "internet de las cosas" han proporcionado nuevas formas de explorar el potencial completo de la respuesta de la demanda, al permitir alterar las cargas de una manera mucho más dinámica y precisa, optimizando así la utilización de los activos de red. Esta tesis se centra en uno de los principales tipos de programas de DR que es la flexibilidad de la demanda. La flexibilidad de la demanda es la capacidad de los clientes del lado de la demanda para ajustar sus perfiles de carga en respuesta a una señal del mercado externo. En los períodos a corto y mediano plazo, los operadores de sistemas de distribución pueden aprovechar la flexibilidad de la demanda para mitigar las congestiones en la red causadas por el aumento de los picos de demanda o la alta penetración de energía renovable. En el período a largo plazo, los distribuidores pueden incluir la flexibilidad de la demanda en su proceso de planificación de expansión de la red para el crecimiento futuro de la demanda. El uso óptimo de la flexibilidad de la demanda puede ayudar a posponer las inversiones necesarias para mejorar la capacidad de las redes. La flexibilidad de la demanda se puede conseguir mediante soluciones basadas en el mercado que pueden ofrecer servicios de flexibilidad rentables para varios agentes del mercado al facilitar la competencia entre diferentes proveedores de flexibilidad. Los reguladores suelen considerar que son los mecanismos de mercado los que dan la solución óptima para la gestión de la flexibilidad. En relación con estos temas, esta tesis propone un marco integral para un mercado de flexibilidad a en la red de distribución, denominado “Flex-DLM” que permite y facilita el comercio de flexibilidad de demanda entre el operador del sistema de distribución, como el principal comprador, y los agregadores, como vendedores que representan a los consumidores flexibles. Se han modelado dos tipos de servicios de flexibilidad de demanda, que son: 1- Flexibilidad a subir (UREG), que corresponde a un requerimiento disminución de carga, y 2- Flexibilidad a bajar (DREG), que corresponde a un requerimiento de aumento de carga. Además, el efecto de rebote, o consumo posterior al uso de la flexibilidad, que es un fenómeno común tras la activación de la flexibilidad de la demanda, se tiene en cuenta para ambos tipos de servicios de flexibilidad. Además, se han modelado las restricciones de la red de distribución, que representan las restricciones de flujo de potencia de la red, que es clave para presentar un modelo realista para el mercado de flexibilidad. En el mercado Flex-DLM propuesto, se considera al distribuidor como el operador responsable de despejar el mercado, al tiempo que se encarga de mitigar las congestiones de la red. El Flex-DLM opera en dos marcos de tiempo: el diario y el tiempo real con el objetivo de proporcionar al distribuidor productos flexibles que puedan ayudarlo en el proceso de gestión de la congestión. Además de esto, la incertidumbre de la demanda se tiene en cuenta para evitar que el distribuidor adquiera cantidades incorrectas de flexibilidad de la demanda. Se introduce una nueva opción en el Flex-DLM del día siguiente, denominado derecho de uso que le permite al distribuidor reservar el derecho de activar la flexibilidad de la demanda durante el período del día anterior para congestiones que tienen poca probabilidad de ocurrencia en el siguiente día de operación. De esta manera, el distribuidor puede recurrir a esta opción en tiempo real si se produce la congestión. Además, se tiene en cuenta la incertidumbre sobre del compromiso de cumplimiento de los clientes con los requerimientos y las cantidades de energía activadas durante el proceso de gestión de la flexibilidad. En esta tesis, se modela asimismo el proceso de toma de decisiones del DSO para optimizar su elección de flexibilidad de demanda y minimizar su costo total. Se llevaron a cabo dos métodos para el modelo de optimización propuesto en este trabajo. El primer método sigue un enfoque determinista, donde el objetivo es optimizar el coste de la flexibilidad para el distribuidor y eliminar el Flex-DLM solo durante el mercado diario , sin tener en cuenta la incertidumbre de la demanda y la de la participación de los consumidores. El segundo método sigue un enfoque probabilístico, que considera la incertidumbre de la demanda durante los períodos diarios y en tiempo real y modela la incertidumbre del compromiso de los clientes. Ambos métodos de optimización se integraron con una herramienta de solución de flujo de potencia óptimo (OPF) para verificar la validez técnica de los servicios de flexibilidad activados y asegurar que el efecto de recuperación no cause más congestiones en la red. La ventaja del marco propuesto es que requiere cambios regulatorios mínimos y no involucra al DSO en ningún comercio de electricidad. Además, el método de optimización propuesto se puede integrar con cualquier herramienta de solución OPF. Se han utiliado diferentes líneas de distribución obtenidos de una red de distribución ubicada en España para verificar la validez del marco propuesto y el proceso de toma de decisiones. Los estudios de caso se dividen en dos partes: 1- La primera parte aplica el marco de flexibilidad propuesto desde una perspectiva determinista y 2- La segunda parte aplica el marco Flex-DLM considerando todas las incertidumbres, que corresponden al enfoque de optimización probabilística. Finalmente, para ayudar al distribuidor en el proceso de planificación a largo plazo de su red local, se lleva a cabo un análisis coste - beneficio para valorar el impacto económico de la implementación de programas de flexibilidad de la demanda como una solución alternativa a las actualizaciones de red convencionales.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Hortensia Elena Amaris Duarte.- Secretario: Milan Prodanovic.- Vocal: Barry Patrick Haye

Effective demand response gathering and deployment in smart grids for intensive renewable integration using aggregation and machine learning

Tesis por compendio de publicaciones.[EN] Distributed generation, namely renewables-based technologies, have emerged as a crucial component in the transition to mitigate the effects of climate change, providing a decentralized approach to electricity production. However, the volatile behavior of distributed generation has created new challenges in maintaining system balance and reliability. In this context, the demand response concept and corresponding programs arise giving the local energy communities prominence. In demand response concept, it is expected an empowerment of the consumer in the electricity sector. This has a significant impact on grid operations and brings complex interactions due to the volatile behavior, privacy concerns, and lack of consumer knowledge in the energy market context. For this, aggregators play a crucial role addressing these challenges. It is crucial to develop tools that allow the aggregators helping consumers to make informed decisions, maximize the benefits of their flexibility resources, and contribute to the overall success of grid operations. This thesis, through innovative solutions and resorting to artificial intelligence models, addresses the integration of renewables, promoting fair participation among all demand response providers. The thesis ultimately results in an innovative decision support system - MAESTRO, the Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. MAESTRO is composed by a set of diversified models that together contribute for handling the complexity of managing energy communities with distributed generation resources, demand response providers, energy storage systems and electric vehicles. This PhD thesis comprises a comprehensive analysis of state-of-the-art techniques, system design and development, experimental results, and key findings. In this research were published twenty-six scientific papers, in both international journals and conference proceedings. Contributions to international projects and Portuguese projects was accomplished. [ES] La generación distribuida, en particular las tecnologías basadas en energías renovables, se ha convertido en un componente crucial en la transición para mitigar los efectos del cambio climático, al proporcionar un enfoque descentralizado para la producción de electricidad. Sin embargo, el comportamiento volátil de la generación distribuida ha generado nuevos desafíos para mantener el equilibrio y la confiabilidad del sistema. En este contexto, surge el concepto de respuesta de la demanda y los programas correspondientes, otorgando prominencia a las comunidades energéticas locales. En el concepto de "respuesta a la demanda" (DR por sus siglas en inglés), se espera un empoderamiento del consumidor en el sector eléctrico. Esto tiene un impacto significativo en la operación de la red y genera interacciones complejas debido al comportamiento volátil, las preocupaciones de privacidad y la falta de conocimiento del consumidor en el contexto del mercado energético. Para esto, los agregadores desempeñan un papel crucial al abordar estos desafíos. Es fundamental desarrollar herramientas que permitan a los agregadores ayudar a los consumidores a tomar decisiones informadas, maximizar los beneficios de sus recursos de flexibilidad y contribuir al éxito general de las operaciones de la red. Esta tesis, a través de soluciones innovadoras y utilizando modelos de inteligencia artificial, aborda la integración de energías renovables, promoviendo una participación justa entre todos los proveedores de respuesta de la demanda. La tesis resulta en última instancia en un sistema de apoyo a la toma de decisiones innovador: MAESTRO, Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. MAESTRO está compuesto por un conjunto de modelos diversificados que contribuyen juntos para manejar la complejidad de la gestión de comunidades energéticas con recursos de generación distribuida, proveedores de respuesta de la demanda, sistemas de almacenamiento de energía y vehículos eléctricos. Esta tesis de doctorado comprende un análisis exhaustivo de las técnicas de vanguardia, el diseño y desarrollo del sistema, los resultados experimentales y los hallazgos clave. En esta investigación se publicaron veintiséis artículos científicos, tanto en revistas internacionales como en actas de conferencias. Se lograron contribuciones a proyectos internacionales y proyectos portugueses. [POR] A produção distribuída, nomeadamente as tecnologias baseadas em energias renováveis, emergiram como um componente crucial na transição para mitigar os efeitos das alterações climáticas, proporcionando uma abordagem descentralizada à produção de eletricidade. No entanto, o comportamento volátil da geração distribuída criou desafios na manutenção do equilíbrio e da fiabilidade do sistema. Nesse contexto, surge o conceito de resposta à procura e os programas correspondentes, conferindo proeminência às comunidades energéticas locais. No conceito de resposta à procura, espera-se um empoderamento do consumidor no setor elétrico. Isso tem um impacto significativo nas operações da rede e gera interações complexas devido ao comportamento volátil, preocupações com a privacidade e falta de conhecimento dos consumidores no contexto do mercado energético. Para isso, os agregadores desempenham um papel crucial ao lidar com esses desafios. É fundamental desenvolver ferramentas que permitam aos agregadores ajudar os consumidores a tomar decisões informadas, maximizar os benefícios de seus recursos de flexibilidade e contribuir para o sucesso global das operações da rede. Esta tese de doutoramento, através de soluções inovadoras e recorrendo a modelos de inteligência artificial, aborda a integração de energias renováveis, promovendo uma participação justa entre todos os fornecedores de resposta à procura. A tese resulta, em última instância, num sistema inovador de apoio à tomada de decisões - MAESTRO, Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. A ferramenta MAESTRO é composta por um conjunto de modelos diversificados que, em conjunto, contribuem para lidar com a complexidade da gestão de comunidades energéticas com recursos de geração distribuída, fornecedores de resposta à procura, sistemas de armazenamento de energia e veículos elétricos. Esta tese de doutoramento abrange uma análise abrangente de técnicas de ponta, design e desenvolvimento do sistema, resultados experimentais e descobertas-chave. Nesta pesquisa, foram publicados vinte e seis artigos científicos, tanto em revistas internacionais como em atas de conferências. Foram realizadas contribuições para projetos internacionais e projetos portugueses