Centralized flexibility services for distribution system operators through distributed flexible resources

Under the context of smart grids within smart cities, increasing distributed generation, consumer empowerment and emerging flexibility services, distribution system operators could benefit by activating flexibility in distribution grids to avoid deploying new infrastructures and grid overloading. The solution offered by this work is an energy management system algorithm capable of activating flexibility behind the prosumer main meter during constrained periods. Therefore, the distribution system operator could compensate grid congestion during high consumption or production periods and increase their renewable generation hosting capacity by using behind-the-meter flexibility during peak production periods.Postprint (published version

Distribution-Level Flexibility Market for Congestion Management

Nowadays, problems facing Distribution System Operators (DSOs) due to demand increase and the wide penetration of renewable energy are usually solved by means of grid reinforcement. However, the smart grid paradigm enables the deployment of demand flexibility for congestion management in distribution grids. This could substitute, or at least postpone, these needed investments. A key role in this scheme is the aggregator, who can act as a "flexibility provider" collecting the available flexibility from the consumers. Under this paradigm, this paper proposes a flexibility market led by the DSO and aimed at solving distribution grid congestions. The proposal also includes a flexibility market clearing algorithm, which is easy to implement, has low computational requirements and considers the energy rebound effect. The proposed design has the advantage of excluding the DSO's need for trading in energy markets. Also, the solution algorithm proposed is fully compatible with already existing grid analysis tools. The proposed electricity market is tested with two case studies from a real Spanish distribution network, where the proposed clearing algorithm is used, and finally, results are presented and discussed

Review on distribution network optimization under uncertainty

With the increase of renewable energy in electricity generation and increased engagement from demand sides, distribution network planning and operation face great challenges in the provision of stable, secure and dedicated service under a high level of uncertainty in network behaviors. Distribution network planning and operation, at the same time, also benefit from the changes of current and future distribution networks in terms of the availability of increased resources, diversity, smartness, controllability and flexibility of the distribution networks. This paper reviews the critical optimization problems faced by distribution planning and operation, including how to cope with these changes, how to integrate an optimization process in a problem-solving framework to efficiently search for optimal strategy and how to optimize sources and flexibilities properly in order to achieve cost-effective operation and provide quality of services as required, among other factors. This paper also discusses the approaches to reduce the heavy computation load when solving large-scale network optimization problems, for instance by integrating the prior knowledge of network configuration in optimization search space. A number of optimization techniques have been reviewed and discussed in the paper. This paper also discusses the changes, challenges and opportunities in future distribution networks, analyzes the possible problems that will be faced by future network planning and operations and discusses the potential strategies to solve these optimization problems

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

New actor types in electricity market simulation models: Deliverable D4.4

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: The modelling of agents in the simulation models and tools is of primary importance if the quality and the validity of the simulation outcomes are at stake. This is the first version of the report that deals with the representation of electricity market actors’ in the agent based models (ABMs) used in TradeRES project. With the AMIRIS, the EMLab-Generation (EMLab), the MASCEM and the RESTrade models being in the centre of the analysis, the subject matter of this report has been the identification of the actors’ characteristics that are already covered by the initial (with respect to the project) version of the models and the presentation of the foreseen modelling enhancements. For serving these goals, agent attributes and representation methods, as found in the literature of agent-driven models, are considered initially. The detailed review of such aspects offers the necessary background and supports the formation of a context that facilitates the mapping of actors’ characteristics to agent modelling approaches. Emphasis is given in several approaches and technics found in the literature for the development of a broader environment, on which part of the later analysis is deployed. Although the ABMs that are used in the project constitute an important part of the literature, they have not been included in the review since they are the subject of another section.N/